ORIGINAL_ARTICLE
Effect of Vetiver grass root system on river bank shear strength parameters, case study: Kor river
River bank erosion imposes sever damages to the adjacent lands annually and changes the river morphology considerably. In this study, the effectiveness of the Vetiver grass root system in enhancement of the soil shear strength and reducing the river bank erosion is examined. Several experiments were carried out to find the effect of Vetiver grass root system on soil cohesion and internal friction angle. Also, variations of morphological characteristics of Vetiver grass root system including RAR, RDR, RDDI and RLD in different depths have been investigated as well as the lateral and vertical distribution of roots. Three different spacing between Vetiver grass plants have been studied and the optimum distance has been proposed. The results showed that Vetiver grass root system increases the soil cohesion and internal friction factor upto 104% and 83%, respectively. Also, it was found that soil cohesion and internal friction factor are best correlated with RAR and RDR, respectively. Finally, an attempt was made to compare the Vetiver grass with several native trees. It was concluded that Vetiver grass is more suitable than common plants and can be considered as an alternative for river bank protection against erosion.
https://ije.ut.ac.ir/article_67500_480fea8e72ad03e234fc3081c09ab05a.pdf
2018-09-23
727
738
10.22059/ije.2018.234283.615
vegetation
River bank protection
Biologic method
Sediment
Bank erosion
Hossein
Hamidifar
hamidifar@shirazu.ac.ir
1
Water Engineering, Shiraz University
LEAD_AUTHOR
Mehdi
Bahrami
mehdibahrami121@gmail.com
2
Fasa University
AUTHOR
Mohaamdjavad
Amiri
amiriboogar@yahoo.com
3
Fasa University
AUTHOR
[1]. Dang MH, Umeda S, Yuhi M. Long-term riverbed response of lower Tedori River, Japan, to sediment extraction and dam construction. Environmental Earth Sciences, 2014; 72(8): 2971-2983.
1
[2]. Balaban SI, Hudson-Edwards KA, Miller JR. A GIS-based method for evaluating sediment storage and transport in large mining-affected river systems. Environmental Earth Sciences, 2015; 74(6): 4685-4698.
2
[3]. Ahmadian-Yazdi M. Effect of vegetation on Tajan-Harirood meander bank erosion. MSc. Thesis, 2000; Gorgan University of Agriculture and Natural Resources. [Persian]
3
[4]. Samadi A, and Amiri-Tokaldani E. River bank mass erosion: Process and Mechanism, University of Tehran Press; 2015. p. 504.
4
[5]. Shirdeli A, Shafai-Bajestan M, Ciacar H. Investigation of the effects of tamarisk and tamarisk aphyllaroots on the stability of seistan river banks. 7th International River Engineering Conference, Shahid Chamran University, 2007; 13-15 Feb 2007, Ahwaz [Persian]
5
[6]. Lin D, Liu W, Lin S. Estimating the effect of shear strength increment due to root on the stability of makino bamboo forest slopeland, Journal of GeoEngineering, 2011; 6(2): 73-88
6
[7]. Dumlao MR, Ramananarivo S, Goyal V, DeJong JT, Waller J, Silk WK. The role of root development of Avena fatua in conferring soil strength. American journal of botany, 2015; 102(7): 1050-1060.
7
[8]. Ghestem M, Veylon G, Bernard A, Vanel Q, Stokes A. Influence of plant root system morphology and architectural traits on soil shear resistance. Plant and soil, 2014; 377(1-2), 43-61.
8
[9]. Islam MS. Application of Vetiver (Vetiveria zizanioides) as a bio-technical slope protection measure–some success stories in Bangladesh. Proceedings of the 6th International Conference on Vetiver, 2015; 5-8 May, Danang City, Vietnam.
9
[10]. Abdullah MN, and Osman N. Soil-root Shear Strength Properties of Some Slope Plants. Sains Malaysiana, 2011; 40(10): 1065–1073
10
[11]. Genet M, Stokes A, Fourcoud T, Norris JE. The influence of plant diversity on slope stability in a moist evergreen deciduous forest. Ecological Engineering, 2010;36: 265-275.
11
[12]. Schwarz M, Preti F, Giadrossich F, Lehmann P, Or D. Quantifying the role of vegetation in slope stability: A case study in Tuscany (Italy). Ecological Engineering 2010; 36: 285-291.
12
[13]. Ebrahimi N, Shirdeli A, Nik-khah-Javan E, Hossein M. Effects of river bed vegetation on flow hydraulics and bed forms. Journal of Watershed Management and Engineering, 2015; 8(2): 182-192. [Persian]
13
[14]. Hemphill RW, Bramley ME. Protection of river and canal banks. CIRIA, Butter Worths, London; 1989.
14
[15]. Shafaei-Bajestan M, Salimi M. Effects of tamaricaceae and popoluse roots on in situ soil shear strength of Karoon river banks. Journal of Agriculture and Natural Resources Science and Technology, 2002; 6(4): 27-40. [Persian]
15
[16]. Shirdeli A. Study of bioengineering methods for river bank stabilization, Journal of Watershed Science and Engineering, 2012; 7(23): 53-62. [Persian]
16
[17]. Dastoorani M, Rajabi-Mohammadi F. Study of mechanical and hydrological effects of river bank vegetation on bank stability, Case Study: Hana River, 3rd National Seminar on water resources management, University of Agriculture and Natural Resources Science, 2011; 10-12 September, Sari, Iran. [Persian]
17
[18]. Troung P, Van T, Pinners E. Vetiver system applications (Technical Reference). 2008.
18
[19]. Niknejad D. Biological control of river bank erosion by Vetivergrass. 11th National Seminar on Irrigation and Vapor reduction. Kerman, Shahid Bahonar University, 2010; 8-10 February, Kerman, Iran. [Persian]
19
[20]. Xie FX. Vetiver for highway stabilization in Jian Young country: demonstration and extension. In; proceedings of the Interventional vetiver workshop, Fuzhow, China; 1997.
20
[21]. Xia HP, Ao HX, Liu SZ, He DQ. Application of the Vetiver grass bioengineering technology for the prevention of highway slippage in southern China. Proc. Ground and Water Bioengineering for Erosion Control and Slope Stabilization, Manila, Philippines April 1999.
21
[22]. Ke C, Feng Z, Wu X, Tu F. Design Principles and Engineering Samples of Applying Vetiver Eco-engineering Technology for Steep Slope and River Bank Stabilisation. In Proceedings of the Third International Vetiver Conference (ICV3), 2003; Guangzhou, China.
22
[23]. Sy M. The vetiver: from nursery to the protection of infrastructures. Proceedings of the Third International Conference on Vetiver and Exhibition, 2003; Guangzhou, China.
23
[24]. Hengchaovanich D, and Nilaweera NS. An assessment of strength properties of vetiver grass roots in relation to slope stabilization. In International Conference on Vetiver, 1996; Chain Kai, Thailand.
24
[25]. Sangab-Zagros consulting Engineers. Evaluation report of geology and morphology of the Kor river. Fars Regional Water Organization; 2008. [Persian]
25
[26]. Shirani H, Haj-Abbasi M, Afyooni M, Hemmat E. Effect of tillage and manure on maize root morphology. Soil and Water Journal, 2008; 23(1): 101-107. [Persian]
26
[27]. Davoudi M, Fatemi-Aqda M. Effect of Diameter and Density of Willow Roots on Shear Resistance of Soils, Geosciences, 2008; 71: 143-148. [Persian]
27
[28]. Shariata Jafari M, Davoudi M, Safaei M and Partoi A. Invetigating the effect of Diospyros lotus root system in soil reinforcement using RDR and RDDI indices. Journal of Watershed Engineering and Management, 2014; 6(2): 107-114. [Persian]
28
[29]. Davoudi M, Fatemi-Aqda M, Noroozi H, Shah-Alipoor GH. Effect of tree root diameter on soil shear strength, 4th seminar on engineering geology and environment, 2004; 24-26 February, Tarbiat Modares University, Tehran, Iran. [Persian]
29
[30]. Vannoppen, W., Poesen, J., De Baets, S., Vanmaercke, M., Peeters, P., & Vandevoorde, B. Effectiveness of plant roots in controlling rill and gully erosion: A case study on vegetation communities on river dikes. In Proceedings of the 4th International Conference on Soil Bio- and Eco-Engineering, 11-14 July 2016,Sydney, Australia.
30
[31]. Day SD, Seiler JR, Persaud N. A comparison of root growth dynamics of silver maple and flowering dogwood in compacted soil at differing soil water contents. Tree Physiology, 2000; 20(4): 257-263.
31
[32]. Goldsmith W. Soil strength reinforcement by plants, International Erosion Control Association (IECA); 2006. p. 5-16.
32
[33]. da Silva EV, Bouillet JP, de Moraes Gonçalves JL, Junior CHA, Trivelin, PCO, Hinsinger P, Jourdan C, Nouvellon Y, Stape JL, Laclau, JP.. Functional specialization of Eucalyptus fine roots: contrasting potential uptake rates for nitrogen, potassium and calcium tracers at varying soil depths. Functional Ecology, 2011; 25: 996–1006.
33
[34]. Maleki S, Naghdi R, Abdi E, Nikooi M. Study of Tuska root effects on soil armoring as a bioenginnering tools. Iranian Forest Journal. 2013; 6 (1): 49-58. [Persian]
34
ORIGINAL_ARTICLE
Comparing Performans of Fuzzy Logic, Artificial Neural Network and Random Forest Models in Transmissivity Estimation of Malekan Plain Aquifer
Estimating aquifer hydrogeological parameters is essential for the studies or management of groundwater resources. There are several different methods such as pumping test, simulation or modeling of groundwater, geophisical modeling to estimate these parameters. Although analysis and evaluation of pumping test data is the best way to achieve this purpose, it is costly, time consuming and the gained results are from limited points. Malekan plain aquifer is one of the marginal plains of Urmia Lake which suffered more ground water declination and Salinization in last decades and it needs qualitative and quantitative management. In this study, artificial neural networks, fuzzy logic and random forest models have been used to estimate the transmission of aquifers and the performance each of these models has been investigated. Inputs of presented models included related geophysical and hydrogeological variables to transmissivity such as transverse resistivity (Rt), electric conductivity (EC), alluvium thickness (B), and hydraulic conductivity (k). Based on the results of all models, random forest model has higher accuracy and ability to predict transmissivity parameter. According to this model, electrical conductivity (EC), aquifer environment (A) and hydraulic gradient (H) parameters were identified as the most important parameters to predict the transmissivity, respectively.
https://ije.ut.ac.ir/article_67501_2c222de3703d8c8ea688fea4ccb8f63c.pdf
2018-09-23
739
751
10.22059/ije.2018.239914.707
Artificial Intelligence
Groundwater
Malekan Plain
Random forest
Transmissivity
hossein
norouzi
hosseinnorouzi168@yahoo.com
1
tabriz university
AUTHOR
Ata Allah
Nadiri
nadiri@tabrizu.ac.ir
2
Assistant Professor of Natural Faculty, University of Tabriz
LEAD_AUTHOR
Asghar
Asghari Moghaddam
moghaddam@tabrizu.ac.ir
3
Department of Earth Sciences, Faculty of Natural Scineces
AUTHOR
Maryam
Norouzi
m.gharekhani90@gmail.com
4
Earth Sciences, Faculty of Natural Sciences, University of Tabriz
AUTHOR
[1]. Chow VT. On the determination of transmissibility and storage coefficient from pumping test data. Transactions. American Geophysical Union. 1952; 33(3): 397-404.
1
[2]. Cooper H, Jacob C E. A generalized graphical method for evaluation formation constants and summarizing well field history. Transactions, American Geophysical Union. 1946; 27(4): 526-534.
2
[3]. Neuman SP. Theory of flow in unconfined aquifers considering delayed response of water table. Journal of Water Resources Research. 1972; 8(4): 1031-1045.
3
[4]. Theis CV. The relationship between the lowering of piezometric surface and the rate and duration of discharge of a well using groundwater storage. Transactions, American Geophysical Union. 1935; 16(2): 519-524.
4
[5]. Samani N, Gohari-Moghadam M, Safavi AA. A simple neural network model for the determination of aquifer parameters. Journal of Hydrology. 2007; 340(1-2): 1-11.
5
[6]. Nadiri AA, Chitsazan N, Frank TC, Moghaddam A. Bayesian Artificial Intelligence Model Averaging for Hydraulic Conductivity Estimation. Journal of Hydrologic Engineering. 2014; 19(3): 520- 532.
6
[7]. Chen CH, Lin Z. A committee machine with empirical formulas for permeability prediction. Journal of Computers and Geosciences. 2006; 32: 485–496.
7
[8]. Chitsazan N, Nadiri AA, Tsai F. Prediction and structural uncertainty analyses of artificial neural networks using hierarchical bayesian model averaging. Journal of Hydrology. 2015; 528: 52-62.
8
[9]. Kadkhodaie A, Amini A. A fuzzy logic approach to estimation hydraulic flow units from well log data: case study from the Ahvaz oilfield in south Iran. Journal of Petroleum Geology. 2009; 32(1): 67-78 67.
9
[10]. Kadkhodaie A, Rezaee MR, Rahimpour-Bonab H. A committee neural network for prediction of normalized oil content from well log data: An example from South Pars Gas Field, Persian Gulf. Journal of Petroleum Science and Engineering. 2009a; 65: 23-32.
10
[11]. Nadiri AA, Asghari Moghaddam A, Tsai F, Fijani E. Hydrogeochemical analysis for Tasuj plain aquifer, Iran. Journal of Earth System Science. 2013; 122(4): 1091-1105.
11
[12]. Pulido CI, Gutiérrez JC. Improved irrigation water demand forecasting using a soft computing hybrid model. Journal of Biosystems Engineering. 2009; 102(2): 202-218.
12
[13]. Rodriguez V, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sánchez J.P. An assessment of the effectiveness of a Random Forest classifier for land-cover classification. ISPRS Journal of Photogram Remote Sens. 2012d; 67: 9 -104.
13
[14]. Breiman L. Random Forests. Machine Learning. 2001; 45(1): 5–32.
14
[15]. Yoo W, Ference BA, Cote ML, Schwartz A. A Comparison of Logistic Regression, Logic Regression, Classification Tree, and Random Forests to Identify Effective Gene-Gene and Gene-Environmental Interactions. International Journal of Applied Science and Technology. 2012; 2(7): 268-274.
15
[16]. Norouzi H, Asghari Mogaddam A, Nadiri AA. Determining vulnerable areas of Malekan Plain Aquifer for Nitrate, Using Random Forest method. Journal of Environmental Studies. 2015; 41(4): 923-94. [In Persian]
16
[17]. Hopfield JJ. Neural network and physical systems with emergent collective computational abilities. Proc. Nat, Academy of scientists. 1982; 79: 2554-2558.
17
[18]. Demuth H, Beale M. Neural Network Toolbox User, s Guide, By the Math Works. Inc Version. 2000; 4: 840pp.
18
[19]. ASCE. Task Committee on Application of Artificial Neural Networks in Hydrology, Part I and II. Journal of Hydrology. 2000; 5(2): 115-137.
19
[20]. Chiu S. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems. 1994; 2(4): 267–278.
20
[21]. Nikravesh M, Aminzadeh F. Soft Computing and Intelligent Data Analysis in Oil Exploration. Part1: Introduction: Fundamentals of Soft Computing. Elsevier, Berkeley, USA. 2003; pp.744.
21
[22]. Quinlan JR. Induction of decision trees. Journal of Machine Learning. 1986; 1(1): 81-106.
22
[23]. Schapire R. The strength of weak learnability. Journal of Machine learning, 1990; 5:197-227.
23
[24]. Kotsiantis S, Pintelas P. Combining bagging and boosting. International Journal of Computational Intelligence. 2004; 1(4): 324–33.
24
[26]. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees, Chapman & Hall/CRC, New York. 1984; pp.744.
25
[26]. Quinlan JR. C4.5 programs for machine learning. San Mateo, CA: Morgan Kaurmann. 1993; 303 pp.
26
[27]. Breiman L. Bagging predictors. Machine Learning. 1996; 24(2): 123–40.
27
[28]. Bellman R. Dynamic programming. Mineola, NY: Dover Publications. 2003; 366 pp.
28
[29]. Guyon I, Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Res. 2003; 3: 1157–82.
29
[30]. Dixon BA. Case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N. Journal of Hydrogeology. 2010; 17(6): 1507–20.
30
[31]. Critto A, Carlon C, Marcomini A. Characterization of contaminated soil and groundwater surrounding an illegal landfill by principal component analysis and kriging. Journal of Environmental Pollution. 2003; 122(2): 235–44.
31
[32]. Harb N, Haddad K, Farkh S. Calculation of transverse resistance to correct aquifer resistivity of groundwater saturated zones, implications for estimating its hydrogeological properties. Lebanese science journal. 2010; 11(1): 105-115.
32
[33]. Valcarce RM, Rodríguez WM. Resolution power of well log geophysics in karst aquifers. Journal of Environmental Hydrology. 2004; 12: 1-7.
33
[34]. Lehmann P, Davis. Evaporation and capillary coupling across vertical textural contrasts in porous media. Journal of Phys, Rev. 2009; 80(4): 44-57
34
[35]. Chehata N, Guo L, Mallet C. Airborne lidar feature selection for urban classification using random forests. International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences. 2009; 39: 207-12.
35
ORIGINAL_ARTICLE
Determination of the Environmental Flow Requirements for the SefidRud River, IRAN
Environmental Flows describes the timing, quality, and quantity of water flows required to sustain ecosystems, human well-being and livelihoods that depend upon them. A review of the present status of environmental flow methodologies revealed the existence of 4 differentiated methodologies. The main purpose of this paper is to assess common methods of environmental flow requirements. According to the standards of the Ministry of Energy of Iran, Tennant introduced as the base method for determining the environmental flows. This method together with wetted perimeter method with three different ideas is applied to the SefidRud. Also, the habitat simulation method, which is a part of the ecological method, is applied to SefidRud River too. Huso Huso fish was chosen as the species river target in this research study. The advantages and limitations of the above mentioned methods are presented as a part of results. 20% and 30% of the annual average flows are suggested for maintaining river health in poor and good conditions, respectively. The combined simulation method habitats and the maximum curvature of the wetted perimeter are recommended as a native method for SefidRud River to determine the minimum ecological flow and improve the ecosystem of the studied river.
https://ije.ut.ac.ir/article_67502_35df20aee8535ba2e3698fa4cdfdf55c.pdf
2018-09-23
753
762
10.22059/ije.2018.240350.711
Environmental flow
ecosystem
target species
Tennant assessment
wetted perimeter
Fatemeh
Fattahpour
fattahpour135@alumni.ut.ac.ir
1
Former MSc Student of Water Resources Engineering, Department of Irrigation and Reclamation Engineering, University of Tehran
AUTHOR
Kumars
Ebrahimi
ebrahimik@ut.ac.ir
2
Professor of Irrigation and Reclamation Engineering Department. University of Tehran
LEAD_AUTHOR
Sogand
Bayat
sogand.bayat@ut.ac.ir
3
Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran
AUTHOR
[1]. Naiman R J, Bunn SE, Nilsson C, Petts GE, Pinay G, Thompson LC. Legitimizing fluvial systems as users of water: an overview. Environmental Management. 2002; 30:455-467.
1
[2]. Arthington A. Saving Rivers in the Third Millennium. Ecology and the Environment Book; 2012.
2
[3]. Dunbar MJ, Acreman MC, Gustard A, Elliott CRN. Overseas Approaches to Setting River Flow Objectives; 1998.
3
[4]. Acreman M, Dunbar MJ. Defining environmental river flow requirements- a review. J. Hydrology and Earth system Sciences. 2004; 8(5): 861-876.
4
[5]. Liu J, Liu Q, Yang H. Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality. Ecological Indicators. 2016; 60: 434-441.
5
[6]. Tharme RE. A global persprctive on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. Published online in Wiley InterScience. 2003; 19:397-441.
6
[7]. Richter BD, Baumgartner JV, Wigington R, Braun DP. How much water does a river need. Freshwater Biology. 1997; 37: 231-249.
7
[8]. Office of Standard and Technical Criteria. Planning and Budget Organization of Iran. Drinking Water Standards. Tehran; 1992. (In Persian)
8
[9]. Tharme RE, King JM. Development of the Building Block Methodology for Instream Flow Assessment, and Supporting Research on the Effects of the Different magnitude Flows on Riverine Ecosystems. Water Research Commission; 1998.
9
[10]. Smakhtin VU, Revenga C, Doll P. A pilot Global Assessment of Environment Water Requirement and Scarcity. International water Resources Association. 2004; 307-317.
10
[11]. Mann JL. Instream flow methodologies: An evaluation of the Tennant method for higher gradient streams in the national forest system lands in the western US. Master Thesis. Department of Forest, Rangeland, and Watershed Stewardship,colorado state university, Colorado; 2006. 143 p.
11
[12]. Watt SP. A methodology for environmental protection of Ontario watercourses with respect to the permit to take water program; 2007. 134p.
12
[13]. Zolfaghari S, Ghanbarpour M, Habibnejad M, Afkhami M. Evaluation and assessment of environmental flow using hydrological methods (Case study: Shadegan wetland). Journal of Watershed Management Science and Engineering. 2009; 3(8): 67-70. (in Persian)
13
[14]. Poff N, Richter B, Arthington A, Bunn S, Naiman R, Kendy E, et al. The ecological li, its of hydrologic alteration (ELOHA), A new framework for developing regional environmental flow standards. J. Freshwater Biol. 2010; 55(1): 147-170.
14
[15]. Jushi KD, Jha DN, Alam A, Srivastava SK, Kumar V, Sharma AP. Environmental Flow requirements of river sone: impact of low discharge on fisheries. Current Science. 2014; 107(3): 478.
15
[16]. Mostafavi S, Yasi M. Evaluation of Environmental Flowsin Rivers Using Hydrological Methods (case study: The Barandozchi River- Urmia Lake Basin). J. water Soil. 2015; 29(5), 1219-1231. (in Persian)
16
[17]. Bayat S, Ebrahimi K., Assessment of Different Environmental Flow Methods. 2th National Iraninan Conference on Hydrology, Shahrekord, Iran, 2017. (in Persian)
17
[18]. Orth DJ, Maughan OE. Evaluation of the “Montana Method” for recommending instream flows in Oklahoma streams. Pro. Okla. Acad. Sci. 1981; 61:62-66.
18
[19]. Tennant DL. Instream flow regimes for fish, wildlife, recreation and environmental resources. Instream Flow Needs. Volume II. American Fisheries Society. Bethesda MD. 1976; 359-373.
19
[20]. King JM, Tharme RE, Villiers DE. Environmental flow assessments for rivers: manual for the Building Block Methodology. Water Research Commission Technology Transfer Report No. TT131/00. Pretoria, South Africa; 2003.
20
[21]. Peres DJ, Cancelliere A, Environmental Flow Assessment Based on Different Metrics of Hydrological Alteration. Water Resources Management. 2016; 30(15).
21
[22]. Annear TC, Conder AL. Relative bias of Several fisheries instream flow methods. North American Journal of Fisheries Management. 1984; 4(4B): 531-539.
22
[23]. Poff NL, Allan JD, Bain MB, Kar JR, Prestegaard kl. The natural flow regime: a paradigm for river conservation and restoration. Bio science, 1997; 47: 769-784.
23
[24]. Gippel CJ, Stewardson MJ. Use of wetted perimeter in defining minimum environmental flows. Regulated Rivers: Research and Management. 1998; 14: 53-67.
24
[25]. Jowett IG. Instream flow methods: a comparison of approaches. Regulated rivers. 1997; 13: 115-127.
25
[26]. Ahmadipour Z, Yasi M. Comparison of echo-hydrologic-hydraulic methods in the assessment of river ecological flow (Nazlou river, Urmia lake basin), Hydraulic science journal. 2015; 9(2): 69-82. (in Persian)
26
[27]. Amini M, Shokouhi A. Analytical solution Determination of the fracture point of the contaminated-discharge environment in the hydraulic method of determining the minimum environmental flow. "Journal of Hydraulic. 2015; 9(1): 27-43. (in Persian)
27
ORIGINAL_ARTICLE
Application of Genetic Algorithm to Optimize the Performance of Adaptive Neural - Fuzzy Inference System in order to predict maximum of air temperature (Case study: Isfahan city)
In this study, the use of genetic optimization algorithm (GA),Particle Swarm(PSO), the ant colony for continuum (ACOR)and differential evolution (DE),to develop and improve the performance of ANFIS were investigated. the monthly maximum temperatures in Isfahan during the period of 64 years (1951-2014), was simulated and analyzed. At first in a sensitivity analysis, the best entries for each prediction period (1 month, 1, 2 and 3 years) were selected. Then, the maximum temperature hybrid models by ANFIS-GA,ANFIS-PSO,ANFIS-DE,ANFIS-ACOR and ANFIS were examined. The performance of each model with regard to R2, RMSE and MAE were evaluated. The results showed that the ANFIS-GA, as the most appropriate model, increased ANFIS performance in R2 to by 0.06, 0.07, 0.08 and 0.12 and RMSE by 0.09, 0.09, 0.16 and 0.1, respectively, in 1 month and 1, 2 and 3 year. After, ANFIS-DE and ANFIS-PSO, respectively, had the best forecasting accuracy. On the other hand, ANFIS showed highest error and lowest R2, as the weakest model. The results showed that the proposed models, which use global search techniques and avoid being trapped in local optimum, could improve the performance of ANFIS favorably.Therefore, these models can be used in other areas related to hydrology and water resources.
https://ije.ut.ac.ir/article_67503_81d2e2f008c1dc4554c502cc93a77480.pdf
2018-09-23
763
775
10.22059/ije.2018.241766.726
Adaptive Neural-Fuzzy Inference System
Hybrid Evolutionary Algorithms
Genetic Algorithm
Local Optimum Solution
Air Temperature
mehran
manoochehri nia
mehran3560@gmail.com
1
M.Sc. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran.
AUTHOR
armin
Azad
armin.azad1@yahoo.com
2
M.Sc. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran
AUTHOR
Saeed
Farzin
saeed.farzin@semnan.ac.ir
3
Assistant Professor, Faculty of Civil Engineering, Semnan University, Semnan
AUTHOR
Hojat
Karami
hkarami@semnan.ac.ir
4
Assistant Professor, Faculty of Civil Engineering, Semnan University, Semnan
LEAD_AUTHOR
[1]. Asakareh H. ARIMA modeling of annual mean temperature of Tabriz city. Geographical Research. 2009; 47: 123-131.
1
[2]. Benavides R, Montes F, Rubio A , Osoro K. Geostatistical modeling of air temperature in a mountainous region of northern Spain. Agricultural and Forest Meteorology. 2007; 146(3-4): 173-188.
2
[3]. Jain AK. Mao J, Mohiuddin KM.. Artificial neural networks: A tutorial. Computer, IEEE. 1996: 31-44.
3
[4]. Peyghami MR, Khanduzi R. Novel MLP neural network with hybrid tabu search algorithm. Neural Network World. 2013; 3(13): 255-270.
4
[5]. Pousinho HMI, Mendes VMF, Catalão JPS. Hybrid PSO-ANFIS Approach for Short-Term Electricity Prices Prediction. In Proceedings of the 2010 PES general meeting, Michigan. 2010: 1-6.
5
[6]. Sheikhan M, Mohammadi N. Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data. Neural computing and applications. 2013;23(3-4): 1185-1194.
6
[7]. Cheng CHT, Niu WJ, Feng ZK, Shen J, Chau KW. Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization. Water. 2015; 7: 4232- 4246.
7
[8]. Jalalkamali A. Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters. Earth Science Informatics. 2015; 8(4): 885-894.
8
[9]. Rezapour Tabari M M. Prediction of River Runoff Using Fuzzy Theory and Direct Search Optimization Algorithm Coupled Model. Arabian Journal for Science and Engineering. 2016; 41(10): 4039-4051.
9
[10]. Behmanesh M, Mohammadi M. Adaptive Neuro-Fuzzy Inference System with Self-Feedback and Imperialist Competitive Learning Algorithm for Chaotic Time Series Prediction. Journal of Computational Intelligence in Electrical Engineering. 2016; 4(7): 13-30.
10
[11]. Azad A, Karami H, Farzin S, Saeedian A, Kashi H, Sayyahi H. Prediction of water quality parameters using ANFIS optimized by intelligence algorithms (Case study: Gorganrood River). KSCE Civil engineering Journal. 2017; 1-8. DOI 10.1007/s12205-017-1703-6. [Persian]
11
[12]. Salahi B, Hoseini SA, Shayeghi H, Sobhani B. Prediction of maximum temperatures using artificial neural network model. Geographic research. 2010; 25(3): 57-78. [Persian]
12
[13]. Tektas M. Weather Forecasting Using ANFIS and ARIMA Models, A Case Study for Istanbul. Environmental Research, Engineering and Management. 2010; 51:5-10.
13
[14]. Ghorbani MA, Kazemi H, Farsadizadeh D, Yousefi P. Prediction of Air Temperature Using Artificial Intelligent Methods. Journal of Engineering and Applied Sciences. 2012; 7(2): 134-142.
14
[15]. Kisi O, Kim S, Shiri J. Estimation of dew point temperature using neuro-fuzzy and neural network techniques. Theoretical and Applied Climatology. 2013; 114(3-4): 365-373.
15
[16]. Daneshmand H, Tavousi T, Khosravi M, Tavakkoli S. Modeling minimum temperature via adaptive 4 neuro-fuzzy inference system method based 5 on spectral analysis of climate indices. Journal of the Saudi Society of Agricultural Sciences. 2015; 14(1): 33-40.
16
[17]. Mohammadi K, Shamshirband Sh, Tong CW, Arif M, Petkovic Ch. A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation. Energy Conversion and Management. 2015; 92: 162-171.
17
[18]. Kisi O, Sanikhani H. Modelling long-term monthly temperatures by several data-driven methods using geographical inputs. International Journal of Climatology. 2015; DOI: 10.1002/joc.4249.
18
[19]. Shafaghi S. Geography of Isfahan. 2nd ed. University of Esfahan. Esfahan. 2003. [Persian]
19
[20]. Zadeh LA. Fuzzy sets. InformationandControl. 1965; 8(3): 338-353.
20
[21]. Jang JSR. ANFIS: Adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions. 1993; 23(3), 665-685.
21
[22]. Storn R, Price K. Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical report, International Computer Science, Berkeley. 1995.
22
[23]. Dashti R, Sattari M T, Nourani V. Performance evaluation of differential evolution algorithm in optimum operating of Eleviyan single-reservoir dam system. Journal of Protection of water and soil resources. 2017; 6(3): 61-76.
23
[24]. Holland JH. Adaption in natural and artificial system. The University of Michigan Press. 1975.
24
[25]. Jaramillo J, Bhadury J, Batta R. On the use of genetic algorithms to solve location problems. Computers & Operations Research. 2002; 29: 761-779.
25
[26]. Eberhart R, Kennedy J. A New Optimizer Using Particle Swarm Theory. Sixth International Symposium on Micro Machine and Human Science, IEEE. 1995.
26
[27]. Golmakani H, Fazel M. Constrained Portfolio Selection using Particle Swarm Optimization. Expert Systems with Applications. 2011; 38: 8327–8335.
27
[28]. Dorigo M. Optimization, Learning and Natural Algorithms. Ph.D Thesis. Dipartimento di Elettronica, Politecnico di Milano, Italy. 1992.
28
[29]. Socha K, Dorigo M. Ant colony optimization for continuous domains. European Journal of Operational Research. 2008; 185: 1155-1173.
29
[30]. Deb K A P, Agarwal S, Meyarivan T. A Fast Elitist Multi-Objective Genetic Algorithm:NSGA-II. IEEE Transactions on Evolutionary Computation.2000; 6: 182-197.
30
ORIGINAL_ARTICLE
Assessment of future runoff trends under multiple climate change scenarios in the Gamasiab river basin
One of the natural characteristics of the Gamasiab River is the probability of occurrence of the flood and its hazard. Hydrological studies under climate change conditions are required to organize and manage it. Because of the necessity of using CMIP5 series models in new researches due to their high accuracy and lack of research using these models in our country, in the present study, four models of CMPI5 series and two scenarios RCP2.6 and RCP8.5 were used for the near future (2049-2020 AD) and the far future (2070-2099 AD). The results show the annual rainfall in five stations would vary from 52.8 to -31.6 percent according to the scenarios and different time periods. The average minimum and maximum monthly temperature at Kermanshah station increases to 2.75 ° C and 2.15 C°, and at Hamadan station, increases to 3.43 C° and 4.26 C°, respectively according to different scenarios. The SWAT model was used to simulate the hydrologic regime. The results while confirming the effectiveness of the SWAT model for simulating of river discharge, showed that changes in runoff rate using the output of the CSIRO-k3.6.0 model under different scenarios would indicate a change from 17.8 to -42.3 percent
https://ije.ut.ac.ir/article_67504_a1402bb5e9cfee8b1b9460f3f84c0e35.pdf
2018-09-23
777
789
10.22059/ije.2018.242453.732
climate change
Gamasiab basin
simulation
Shahab oddin
Zarezade Mehrizi
shahabazar13@gmail.com
1
PhD Candidate in Watershed managment
AUTHOR
Asadollah
Khoorani
agroclimatologist@gmail.com
2
Geography Department,Hormozgan University,Bandar Abbass,Islamic Republic of Iran
AUTHOR
Javad
Bazrafshan
jbazr@ut.ac.ir
3
Department of Irrigation and Reclamation, Faculty of Agricultural Engineering and Technology,College of Agriculture & Natural Resources,University of Tehran, Karaj
AUTHOR
Omolbanin
Bazrafshan
bazrafshan1361@gmail.com
4
Department of Watershed management and range management, Faculty of Agricultural Engineering, Hormozgan University, Hormozgan
AUTHOR
[1]. IPCC expert meeting report. towards new scenarios for analysis of emissions, climatechange, impacts, and response strategies. 19–21 September, 2007 Noordwijkerhout, The Netherlands
1
[2]. Herting, E. and J. Jacobeit., 2008,“Downscaling Future Climate change: Temperature scenarios for the Mediterrnean Area”, Global and Planetary hange 63. 127- 131.
2
[3]. Ozkul, S., 2009,“Assessment of climate change eifects in Aegean River Basins: The case Of Gediz Buyuk Menders Basins”, J. climate change
3
[4]. Harmsen, E. W., Miller, N. L., Schelgel, N. J. and Gonzalez, J.E.2009,“Seasonal climate change Impactes on Evaportranspiration, Percipitation deficit and crop Yield in Puer Rico”,J. Of Agricultural Water Management, 96. 1085- 1095.
4
[5]. Babaean A, Najafinik Z, Abbasi F, Nohandan M, Adab H. 2009. "Assessment of the country's climate change during the period 2010-2039 using the general circulation model of ECHO-G. Geography and Development,2009; 16: 152-135.
5
[6]. Revelle R. R, Waggoner P. E.1983. Effects of carbon dioxideinduced climate change on water supplies in western of United States. Climate changing Nat. Acad. Washangton D. C. 1983.
6
[7]. Wilby R, Harris I. A frame work for assessing uncertainties in climate change impacts: low flow scenarios for the River Thames, UK. Water Resources Research. 2006; 42
7
[8]. Jahanbakhsh S, Khorshid Dust A.M, Alinejed M.H, Purasghar F. The Impact of Climate Change on Temperature and Precipitation Considering the Uncertainty of Climate Models and Scenarios(Case study of Urmia Shahr-e Chah Basin). Hydrogeomorphology Journal. 2016; 7: 107-122. [Persian]
8
[9]. Kamal A.R, Massah Bavani A.R. Evaluation of uncertainty of AR4-AOGCM models and hydrologic models in estimating temperature, precipitation and the runoff of Qaraosso basin under climate change. Journal of Water Research of Iran. 2011; 5(9): 39-50. [Persian]
9
[10]. Eghdamirad S, Johnson F, Woldemeskel F, Sharma A. Quantifying the sources of uncertainty in upper air climate variables. Journal of Geophysical Research: Atmospheres. 2016; 27;121(8):3859-74.
10
[11]. Elashamy M. E., Wheater, H. S., Huntingford, C. Evaluation of the rainfall Companent of Weather generator for climate change Studies. Journal of Hydrology. 2005; 326: 1-24.
11
[12]. Schimidli, H, Goodess C. M., Frei C, Haylouk M. R., Schmith S. Statistical and dynamical downscaling Precipitation: An Evaluation and Camparison of scenario for the European Alps. Journal of Geophysical Research, 2007; 112: 1-20.
12
[13]. IPCC. Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker T.F, Qin G, Plattner M, Tignor S.K, Allen J, Boschung A, et al. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
13
[14]. Basheer A, Lu H, Omer A, Ali A, Abdelgader A. Impacts of climate change under CMIP5 RCP scenarios on the stream flow in the Dinder River and ecosystem habitats in Dinder National Park, Sudan. Hydrol. Earth Syst. Sci. 2016; 20: 1331–1353.
14
[15]. Papadimitriou L, Koutroulis L, Grillakis M, Tsanis I. High-end climate change impact on European runoff and low flows – exploring the effects of forcing biases. Hydrol. Earth Syst. Sci. 2016; 20: 1785–1808.
15
[16]. Hoang L, Lauri H, Kummu M, Koponen J, Michelle T, Vliet H, et al. Mekong River flow and hydrological extremes under climate change. Hydrol. Earth Syst. Sci. 2016; 20: 3027–3041.
16
[17]. Arias R, Blanco M, Taboada-Castro M, Nunes J, Keizer J. Water Resources Response to Changes in Temperature, Rainfall and CO2 Concentration: A First Approach in NW Spain. Water. 2014; 6(10), 3049-3067; doi:10.3390/w6103049
17
[18]. Saha P. P., Zeleke K, Hafeez M. Streamflow modeling in a fluctuant climate using SWAT: Yass River catchment in south eastern Australia. Environmental Earth Sciences. 2014; 71(12): 5241–5254
18
[19]. Xu H, Luo Y. Climate change and its impacts on river discharge in two climate regions in China. Hydrol. Earth Syst. Sci. 2015; 19, 4609–4618.
19
[20]. IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.
20
[21]. Ministry of Agriculture. Comprehensive Plan for the Recovery and Development of Agriculture and Natural Resources in the Karkheh and Dez River Basin. 1996;Volume 1, Surface Water, Planning and Support Deputy, Tehran.
21
[22]. Abbaspour K. 2007. User manual for SWAT-CUP, SWAT calibration and uncertainty analysis programs. Eawag: Swiss Fed. Inst. Of Aquat. Sci. and Technol. Du¨bendorf, Switzerland.
22
[23]. Neitsch S, Arnold L, Kiniry G, Williams J. Soil and Water Assessment Tool, Userʼs Manual, Version 2000-2002
23
[24]. Abbaspour K, Rouholahnejad E, Vaghefi S, Srinivasan R, Yang H, Klove B. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology. 2015; 524:733–752
24
[25]. Donizete R, Pereiraa A, Martinezb F, Pruskib D. Hydrological simulation in a basin of typical tropical climate and soil using the SWAT model part I: Calibration and validation tests. Journal of Hydrology: Regional Studies. 2016; 7: 14–37
25
[26]. Kepner W, Hernandez M, Semmens D, Goodrich D. The Use of Scenario Analysis to Assess Future Landscape Change on Watershed Condition in the Pacific Northwest (USA). Use of Landscape Sciences for the Assessment of Environmental Security. 2008; 237-261.
26
[27]. Wang L, Ranasinghe S, M van P, Gelder J, Vrijling K. Comparison of empirical statistical methods for downscaling daily climate projections from CMIP5 GCMs: a case study of the Huai River Basin, China. International journal of climatology Int. J. Climatol. 2016; 36: 145–164
27
[28]. Ho C, Stephenson D, Collins M, Ferro CAT, Brown S. Calibration strategies: a source of additional uncertainty in climate change projections. Bull. Am. Meteorol. Soc. 2012; 93(1): 21–26.
28
[29]. Zahabiyun B, Goodarzi M, Massah A. Application of SWAT model in estimating basin runoff in future periods affected by climate change. Journal of Climatology Research. 2010; 3and4. [Persian]
29
[30]. Jones P, Hulme M, alculating regional climatic times series for temperature and precipitation: methods and illustrations. International journal of climatology. 1996; 16: 361-377
30
ORIGINAL_ARTICLE
Hydrogeochemistry evaluation of Salmas plain aquifer using multivariate statistical methods
For better understand of hydrogeochemical processes in the Salmas plain aquifer, this study adopted graphical methods and multivariate statistical techniques to analyze groundwater samples. The results of the Piper diagram and expanded Durov diagram reveals that the major groundwater type is Ca-(Mg)-HCO3 and mixing groundwater type exists in southeast of the Salmas plain. Hierarchical Cluster Analysis (HCA) identified five classes of groundwater type (HC1 to HC5). The hierarchical cluster analysis is able to show the influence of nitrate concentration in classification while the graphical methods cannot. The Stiff diagrams of five classes (HC1 to HC5) show three different sources of groundwater samples.The HC1 to HC3 classes indicate groundwater with limestone and dolomite origin. In HC4 class, Na+ and Clˉ are the dominate ions in groundwater samples and shows saline waters. The HC5 shows mixing groundwater. Using Factor Analysis (FA), we identified three factors that accounted for 85.03% of the total variance of the dataset. Factors 1 and 2 are reflected the natural hydrogeochemical processes and factor 3 is anthropogenic in the Salmas plain
https://ije.ut.ac.ir/article_67505_602ac7244e58fe9a8aa860076763b0d7.pdf
2018-09-23
791
800
10.22059/ije.2018.242803.737
Groundwater
factor analysis
Hierarchical cluster analysis
Hydrogeochemistry
Keyvan
Naderi
keiwan.naderi@yahoo.com
1
Earth science department, faculty of natural science, University of Tabriz, Tabriz, Iran
AUTHOR
Ata Allah
Nadiri
nadiri@tabrizu.ac.ir
2
Assistant Professor of Natural Faculty, University of Tabriz
LEAD_AUTHOR
Asghar
Asghari Moghaddam
moghaddam@tabrizu.ac.ir
3
professor, faculty of natural science, university of Tabriz, Tabriz, Iran
AUTHOR
Mehdi
Kord
m.kord@uok.ac.ir
4
Earth Science, faculty of natural science, university of Kurdistan, Sanandej, Iran
AUTHOR
[1]. Hounslow AW. Water Quality Data Analysis and Interpretation. 1nd ed. Florida: CKC press; 1995.
1
[2]. Jalali L, Moghaddam AA. Detection of hydrogeochemical status and salinity trend in Khoy plain aquifer by statistical and hydrochemical methods. Journal of Environmental Studies. 2013; 39(2): 113-122 [Persian].
2
[3]. Aghazadeh N, Moghaddam AA. Investigation of hydrochemical characteristics of groundwater in the Harzandat aquifer, Northwest of Iran. Environmental Monitoring and Assessment. 2011; 176:183-195.
3
[4]. Piper AM. A graphic procedure in the geochemical interpretation of water analyses, Transactions American Geophysical Union. 1994; 25: 914-923.
4
[5]. Fijani E, Moghaddam AA, Tsai FTC, Tayfur G. Analysis and assessment of hydrochemical characteristics of Maragheh-Bonab plain aquifer, Northwest of Iran, Journal of Water Resource Management. 2017; 31(3): 765-780.
5
[6]. Singhal BBS, Gupta RP. Applied hydrogeology of fractured rocks. 1nd ed. Dordrecht: Kluwer Academic Publishers; 1999.
6
[7]. Stiff HA. The interpretation of chemical water analysis by means of patterns. Journal of Petroleum Technology. 1951; 3(10): 60-62.
7
[8]. Dalton MG, Upchurch SG. Interpretation of hydrochemical faces by factor analysis, Journal of Groundwater. 1978; 16(4): 228-233.
8
[9]. Reghunath R, Murthy TRS, Raghvan BR. The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India. Journal of Water Research. 2002; 36: 2437-2442.
9
[10]. Chen K, Jiao JJ, Huang J, Huang R. Multivariate statistical evaluation of trace elements in groundwater in coastal area in Shenzhen, China. Journal of Environmental Pollution. 2007; 147(3): 771-780.
10
[11]. Cloutier V, Lefebvre R, Therrien R, Savard MM. Multivariate statistical analysis of geochemical data as indicative of the hydrogeochemical evolution of groundwater in a sedimentary rock aquifer system. Journal of Hydrology. 2008; 353(4): 294-313.
11
[12]. Nadiri AA, Moghaddam AA, Tsai FTC, Fijani E. Hydrogeochemical analysis for Tasuj plain aquifer, Iran. Journal of Earth System Science. 2013; 122(4): 1091-1105.
12
[13]. Nadiri AA, Moghaddam AA, Sadeghiaghdam F, Naderi K. The assessment of salinity and arsenic as the destructive factors affecting on surface and groundwater quality of Sahand dam water basin. Journal of Hydrogeomorphology. 2017; 1(4): 79-99 [Persian].
13
[14]. Voundouris K, Panagopoulos A, Koumantakis J. Multivariate statistical analysis in the assessment of hydrochemistry of the Northern Korinthia Prefecture Alluvial Aquifer System (Peloponnese, Greece). Journal of Natural Resource Research. 2000; 9(2): 135-145.
14
[15]. Liu CW, Lin KH, Kuo YM. Application of factor analysis in the assessment of groundwater quality in a black food disease area in Taiwan. Journal of Science of Total Environment. 2003; 313: 77-89.
15
[16]. Khodabandeh A, Soltani A, Sartipi A. Geological map of the Salmas, Scale: 1/100000. Geological Survey of Iran. 1996 [Persian].
16
[17]. World Health Organization (WHO). A framework for safe water drinking water. Guidelines for drinking water quality recommendation. 3th ed. Geneva: WHO press, 2006.p. 22-35.
17
[18]. Todd DK, Mays LW. Groundwater Hydrology. 3nd ed. New York: John Wiley & Sons Press; 2005.
18
[19]. Lloyd IW. The hydrochemistry of the aquifers of northeastern Jordan. Journal of Hydrology. 1965; 3(3-4): 319-330.
19
[20]. Lloyd JW, Heathcote JA. Natural inorganic hydrochemistry in relation to groundwater-An introduction, 1nd ed. Oxford: Clarrendon Press; 1985.
20
[21]. Kaiser HF. The Varimax Criterion for analytic rotation in factor analysis. Journal of Psychometrical. 1958; 23(3): 187-200.
21
ORIGINAL_ARTICLE
Using the WQI method and the Man-Kendall test to assess the qualitative and quantitative status of groundwater aquifers (case study: Sarkhoon plain, Hormozgan province)
Lack of sustained access to water resources will lead to social disputes and economic disruption. This issue is currently one of the serious problems in Sarkhoon watershed, Hormozgan province. In this regard, the WQI method and the Man-Kendall test have been used to determine the water quality class andto observe the trend in the studied variables, respectively. The results showed that a significant and definite decrease in the quality and level of groundwater in the Sarkhoon plain. The obtained trend for declining water level in the plain had a coefficient of variance of 0.03 and S-Mann-Kendall statistic of -277 at 99.9% significance level. The slope of trend obtained for water level drop and the increase in WQI values, which indicates a decrease in water quality, were -33.3 and -42.3, respectively. The reported values for the slope of trend represent a decrease of 33 centimeters and an increase of 3/4 WQI unit per year.
https://ije.ut.ac.ir/article_67506_31cd26b7fca8b9a829e931c90f567f36.pdf
2018-09-23
801
811
10.22059/ije.2018.244156.761
Groundwater
Man-Kendal
Trend Analysis
WQI Method
sarkhoon plain
Behzad
Adeli
adeli2info@gmail.com
1
دانشجوی دکتری آبخیزداری، دانشگاه هرمزگان
AUTHOR
hanane
kangarani
kangarani@ut.ac.ir
2
hormozgan university
LEAD_AUTHOR
amir
sadodin
amir.sadoddin@gmail.com
3
gorgan/iran
AUTHOR
omolbanin
bazrafshan
bazrafshan1361@gmail.com
4
hormogan/iran
AUTHOR
mohsen
armin
mohsenarmin2007@gmail.com
5
yasuj/iran
AUTHOR
[1]. Adamowski, J.,Chan H. F. A wavelet neural network conjunction model for groundwater level forecasting." Journal of Hydrology. 2011; 407(1): 28-40.
1
[2]. Nazari, R., Joodavi, A. Applied Flow and contaminant Transport Modeling in Aquifer. Aftabe Alamtab Issue, 2014; 230p. (In Persian).
2
[3]. Li, X.D., Liu, C.Q., Harue, M., Li, S.L. Liu, X.L. The use of environmental isotopic (C, Sr, S) and hydrochemical tracers to characterize anthropogenic effects on karst groundwater quality: a case study of the Shuicheng Basin, SW China. Applied Geochemistry, 2010; 25(12): 1924-1936.
3
[4]. Vijay, R., Khobragade, P., Mohapatra P K. Assessment of groundwater quality in Puri City, India: an impact of anthropo-genic activities. Environ Monit Assess. 2011; 177(1–4):409–418.
4
[5]. Fujita, M., Suzuki, J., Sato, D., Kuwahara, Y., Yokoki, H. and Kayanne, H. anthropogenic impacts on water quality of the lagoonal coast of Fongafale Islet, Funafuti Atoll, Tuvalu. Sustainability science, 2013; 8(3), pp.381-390.
5
[6]. Gigloo, F., Najafinejad, A., Moghani Bilehsavar, V., Ghivasi A. Evaluation of water quality variation of Zarringol River, Golestan province. Journal of Water and Soil Conservation. 2013; 20(1): 77-96pp.
6
[7]. Najafi, N. A Framework for Groundwater Quality Assessment. Issue 620. 2011.
7
[8]. Mehri, S., Alshayikh, A., Javadadeh, Z. An Assessment of Changes in Groundwater Quality and Groundwater Levels in Lake Urmia Basin. Iranian Journal of EcoHydrology. 2015; 2(4): 395-404.
8
[9]. Noohegar, A., Reahi, F., Kamangar, M. Identification of suitable flood spreading areas by using groundwater sustainable development approach. (Case Study: Sarkhoon plain). Journal of Environmental Studies. 2016; 42 (1): 33-48.
9
[10]. Panda, D. K., A. Mishra, S. Jena, B. James and A. Kumar. "The influence of drought and anthropogenic effects on groundwater levels in Orissa, India." Journal of hydrology. 2007; 343(3): 140-153.
10
[11]. Zeleňáková M, Purcz P, Oravcová A. Trends in water quality in Laborec River, Slovakia. Procedia Engineering. 2015; 119:1161-70.
11
[12]. Sun W, Xia C, Xu M, Guo J, Sun G. Application of modified water quality indices as indicators to assess the spatial and temporal trends of water quality in the Dongjiang River. Ecological Indicators. 2016; 66:306-12.
12
[13]. Daneshvar-Vosuogi, F., Dinpazhoh, Y., Aalami, M.T. Effect of Drought on Groundwater Level in the Past Two Decades (Case study: Ardebil Plain). J. Soil Water Sci. 2010; 21: 4. 165-179. (In Persian)
13
[14]. .Ekrami, M., Sharifi, Z., and Ekhtesasi, M. Investigation of qualitative andquantitativechanges of groundwater resources in Yazd-Ardakan Plain in decade of 2000-2009. J.Department of hygiene in Yazd. 2011; 10: 82-91. (In Persian).
14
[15]. NaderianFar, M., Ansari, H., Ziaie, A., Davari, K. Evaluating the Groundwater Level Fluctuations under Different Climatic Conditions in the Basin Neyshabour. Journal of Irrigation & Water Engineering. 2011; 1(1): 23-37. (In Persian).
15
[16]. Choobin, B., Malekian, A. Relationship between Fluctuations in the Water and Aquifer Salinization (Case Study: Aquifer Aspas-Fars Provinece). Journal of Desert Management. 2013; 1: 13-26pp. (In Persian).
16
[17]. Khoorani, A., Khajeh, M., 2016. Investigating the Simultaneous between Drought Trends and groundwater Level Reduction (Case Study: Darab Plain). The Journal of Spatial Planing. 2016; 18(2): 58-79pp. (In Persian).
17
[18]. Samadi, R., Behmanesh, J., Rezaei, H. Investigating Trends in Groundwater Level. (Case Study: Urmia Plain). Journah of Water and soil conservation. 2015; 22 (4): 67-84. (In Persian).
18
[19]. . Samadi, J. Spatial-Temporal Modeling of Groundwater Level Variations in Urban and Rural Area - Kashan Aquifer Using GIS Techniques. Journal of Environmental Science&Technology. 2017; 19 (1):65-77pp. (In Persian).
19
[20]. Pasquini, A.I., Lecomte, K.L., Piovano, E.L., Depetris, P.Y. Recent rainfall and runoff variability in central Argentina. Quaternary International. 2006; 158: 1. 127-139.
20
[21]. Sobuohi, J., and Soltani, S. Trend analysis of climate factors in large cities of Iran. J. Sci. Technol. Agric. Natur. Resour. 2008; 12: 303-321. (In Persian)
21
[22]. Yidana, S. M. & Yidana, A. Assessing water quality using water quality index and multivariate analysis. Environmental Earth Sciences. 2010; 59, 1461-1473.
22
[23]. Sahu, P., & Sikdar, P. Hydrochemical framework of the aquifer in and around East Kolkata Wetlands, West Bengal, India. Environmental Geology; 2008, 55, 823-835.
23
[24]. - Ramakrishnaiah C. R., Sadashivaiah C. and Ranganna G. Assessment of Water Quality Index for the Groundwater in Tumkur Taluk, Karnataka State, India. E-Journal of Chemistry; 2009, 6(2): 523-530.
24
[25]. Zare Abyane, H., Bayat Varkeshi, M., Maruofi, S. Investigation of groundwaterfluctuations in Malayer Plain. J. Soil Water Sci; 2012 22: 2. 173-190. (In Persian)
25
[26]. .Daneshvar Vosuogi, F., Dinpazhoh, Y., Aalami, M.T., Gorbani, M.A. Analysis of trend of changes in groundwater quality of Ardebil plain using Man-Kendal non-parametric tests. J. Civil Engin. Environ. 2011; 40: 3. 13-23. (In Persian)
26
[27]. Mann, H.B. Nonparametric tests against trend. Econometrica, 13:245-259, 1945.
27
[28]. Kendall M.G. Rank Correlation Methods. Griffin, London, UK, 1975.
28
[29]. Aziz J. J., Ling M., Rifai H. S., Newell C. J. Gonzales J. R., (). MAROS: A decision support system for optimizing monitoring plans. Groundwater. 2003; 41: 355-367.
29
ORIGINAL_ARTICLE
Prioritization of Flood Inundation sub-watersheds of Maharlo Watershed in Fars Province Using Morphometric Parameters and VIKOR Decision Making Model
The current study aims to prioritize of spatial flooding of Maharlo Sub-watersheds using morphometric parameters and VIKOR decision model. So, to select 13 morphometric parameters including slope, drainage density, stream frequency, constant of channel maintenance, drainage texture rate, ruggedness number, circularity ratio, compactness coefficient, relief ratio, stream length, form factor, elongation ratio, and coefficient of shape, and 1 climatic parameter including rain have been used to determine the weight of the parameters of the AHP. The results showed that the morphometric parameters such as slope and drainage density, and the climatic parameter of the rain, with values of (0.206, 0.165 and 0.134) had the most weight and effect on flood event, while the least weight and consequently the least effect on the coefficient is related to shape (0.122) factor. Also, for prioritizing used from 53 sub-watersheds based on VIKOR decision model. The results showed that sub-watershed 34 based on prioritization of flood first ranked (0.082), The second sub- watershed 31 (0.110) and sub- watershed 12 third ranked (0.129) given specialty, which must have to be prioritized for management operations. While sub-watershed 42 has the last ranked (1) in prioritization of flood, which is indicate low sensitivity to flood events.
https://ije.ut.ac.ir/article_67507_4c582492424f7d9f5db2f07b47e5d796.pdf
2018-09-23
813
827
10.22059/ije.2018.244246.763
Prioritization of flood
Morphometry parameters
Analysis Hierarchical Process
Vikor Model
Maharlo Watershed
Mahdis
Amiri
mahdisamiri94@gmail.com
1
M.Sc student in Shiraz University
AUTHOR
Hamidreza
Pourghasemi
hamidreza.pourghasemi@yahoo.com
2
استادیار بخش مهندسی منابع طبیعی و محیط زیست، دانشکدۀ کشاورزی، دانشگاه شیراز
LEAD_AUTHOR
Alireza
Arabameri
alireza.ameri91@yahoo.com
3
PhD Student in Geomorphology, Tarbiat Modares University
AUTHOR
[1]. Badri B, Zare R, Honarbakhsh A, Atashkhar, F. Prioritization of flood potential Beheshtabad Sub- watershed. Journal of Geographical Studies. 2016; 48(1): 143-158 [Persion].
1
[2]. Dovonce E. A physically based distrinbuted hydrologic model. Master of Science Thesis, the Pennsylvania State University; 2000.
2
[3]. Amani M, Najafi nejad A. Prioritization of Sub-Watersheds based on Morphometric Analysis, GIS and RS Techniques: Lohandar Watershed, Golestan Province. Journal of Watershed Management Research. 2014; 9(5): 1-15 [Persion].
3
[4]. Mohammadi A, Ahmadi H. Prioritizing Sub-watershed to aim present management Watershed Reduction Programs (case study: Marof watershed). Journal of Geography of the land. 2011; 29: 69-77. [Persion]
4
[5]. Aher P, Adinarayana J, Gorantiwar SD. Quantification of morphometric characterization and prioritization for management planning in semi-arid tropics of India: A remote sensing and GIS approach. Journal of Hydrology. 2014; 511. 850-860.
5
[6]. Kumar R, Kumar S, Lohani A, Nema R, Singh R. Evaluation of geomorphological characteristics of a catchment using GIS. GIS India. 2000; 9(3): 13–17.
6
[7]. Leskens JG, Brugnach M, Hoekstra AY, Schuurmans W. Why are decision flood disaster management so poorly supported by information from flood models. Environmental Modeling & Software. 2014; 53: 53-61.
7
[8]. Kolawole OM, Olayami A.B, Ajayi KT. Managing Flood in Nigerian Cities: Risk Analysis and Adaptation Options-Ilorin City as a Case Study. Scholars Research Library. 2011; 3(1): 17-24.
8
[9]. Tingsanchali T. Urban flood disaster management. Procedia Engineering. 2012; 32: 25-37.
9
[10]. Djrodjetive B, Bruck, S. System Approach to the Selection of Priority Areas of Erosion Control with Emphasis on the Implication of the Water Resources Subsystem. River Sedimentation Conference, Beijing, CHINA. 1989; 1547-1554.
10
[11]. Chowdary VM, Chakraborthy D, Jeyaram A, Krishna Murthy YVN, Sharma JR, Dadhwal VK. Multi-Criteria Decision Making Approach for Watershed Prioritization Using Analytic Hierarchy Process Technique and GIS. Water Resource Management. 2013; 27; 3555-3571.
11
[12]. Jang T, Vellidis G, Hyman JB, Brook E, Kurkalova LA. Impact of socioeconomic factors on synoptic assessment for prioritizing BMP implementation to reduce sediment load. In: ASABE Annual International Meeting Louisville, Kentucky. 2011; 7-10.
12
[13]. Badar B, Romshoo SA, Khan MA. Integrating biophysical and socioeconomic information for prioritizing watersheds in a Kashmir Himalayan lake: a remote sensing and GIS approach. Environmental Monitoring and Assessment. 2013; 185: 6419-6445.
13
[14]. Melton MA. Correlations structure of morphometric properties of drainage systems and their controlling agents. Journal of Geology. 1958; 66: 442-460.
14
[15]. Zehtabian GH, Ghodosi J, Ahmadi H, Khalili zade M. Investigate the priority of the flood potential of the watershed and determine the flood generating (Case study: Marme watershed, Fars province). Physical Geography Research Quarterly. 2009; 6: 27-38. [Persion]
15
[16]. Grohmann CH. Morphometric analysis in geographic information systems: applications of free software GRASS and R Star. Computer and Geoscience. 2004; 30 (10): 1055-1067.
16
[17]. Khan M, Gupta V, Moharana P. Watershed prioritization using remote sensing and geographical information system: a case study from Guhiya, India. Journal of Arid Environments. 2001; 49; 465-475.
17
[18]. Biswas S, Sudhakar S, Desai VR. Remote sensing and geographic information system based approach for watershed conservation. Survey Engineering. 2002; 128: 108 - 124.
18
[19]. Thakkar A, Dhiman S. Morphometric analysis and prioritization of miniwatersheds in a Mohr watershed, Gujarat using remote sensing and GIS techniques. Journal of the Indian society of Remote Sensing. 2007; 35 (4). 313–321.
19
[20]. Sharma S, Tignath S, Mishra S. Morphometric analysis of drainage basin using GIS approach. JNKVV Res J. 2008; 42(1). 88–92.
20
[21]. Saghafian B, Farazjoo H, Bozorgy B, Yazdandoost F. Flood intensification due to changes in land use. Water Resources Management, 2008; 22. 1051-1067. [Persion]
21
[22]. Avinash K, Jayappa K, Deepika B. (2011). Prioritization of sub-basins based on geomorphology and morphometric analysis using remote sensing and geographic information system (GIS) techniques. Geocarto International. 2011; 26(7): 569-592.
22
[23]. Chandrashekara H, Lokeshb K, Sameenac M, roopad J, rangannae G. GIS –Based Morphometric Analysis of Two Reservoir Catchments of Arkavati River, Ramanagaram District, Karnataka. Aquatic Procedia, INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015). 2015; 4: 1345 – 1353.
23
[24]. Jee Omar P. Geomatics Techniques Based Significance of Morphometric Analysis in Prioritization of Watershed. International Journal of Enhanced Research in Science Technology and Engineering. 2015; 4(1): 24-13.
24
[25]. Fallah M, Mohammadi M, Kavian K. Prioritization of Sub-watershedsusing Morphometric and LandUse change Analysis. Ecohydrology. 2015; 3(2): 261-274. [Persian]
25
[26]. Rahmati O, Tahmasebipour N, Pourghasemi HR. Sub-watershed flooding prioritization using morphometric and correlation analysis (Case study: Golestan Watershed). Ecohydrology. 2015; 2. 151-161. [Persian]
26
[27]. Razavi zade S, Shahedi K. Tleghan Sub-watershed flooding prioritization using From the combination AHP and TOPSIS. Quarterly journal of natural resources ecosystems of Iran. 2017; 7(4): 33-46. [Persian]
27
[28]. Adhami M, Sadeghi HM. Sub-watershed prioritization based on sediment yield using game theory. Journal of hydrology. 2016; 541: 977-987.
28
[29]. Arab Ameri AR, Pourghasemi HM, Cerda A. Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models. Science of the Total Enviroment. 2017; 613-614: 1385-1400.
29
[30]. Mesbah H, Shafei F, Fakhari zade E. Prediction of the Effect of Watershed Implementation on Flood in Maharlou Watershed, Case Study: Sadra sub-watershed. Second conference management watershed and water resources, Kerman, Iran. 2004; 1-4. [Persian]
30
[31]. Javed A, Khanday MY, Ahmed R. Prioritization of watersheds based on morphometric and landuse analysis using RS and GIS techniques. Journal of the Indian society of Remote Sensing. 2009; 37: 261-274.
31
[32]. Pandey A, Chawdary VM, Mal BC. Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and RS. Water Resource Manage. 2007; 21: 729-746.
32
[33]. Horton RE. Erosional development of streams and their drainage basins; hydrological approach to quantitative morphology. Bulletin of the Geological Society of America. 1945; 56: 275–370.
33
[34]. Srivastava VK. Role of GIS in natural resources management. In: Thakur, B. (Ed.), Perspectives in Resource Management in Developing Countries. Concept Publishing Company. New Delhi. 2003; 479–484.
34
[35]. Schumn SA. Evolution of drainage systems and slopes in badland, at Perth Amboy, New Jersey. Bulletin of the Geological Society of America. 1956; 67. 597–646.
35
[36]. Horton RE. Drainage basin characteristics. Trans. Am. Geophys. Union. 1932; 13. 350–361.
36
[37]. Chen LY, Wang TC. Optimizing partners choice in IS/IT outsourcing projects: The strategicdecision of fuzzy VIKOR. International Journal of. Production Economics. 2009; 120(1): 1-12.
37
[38]. Opricovic S, Tzeng G. Extended VIKOR method in comparison with outranking methods, European Journal of Operational Research. European Journal of Operational Research. 2006;, pp 514-529.
38
[39]. Saaty TL. The Analytic Hierarchy Process. Mc Graw Hill Company, New York. 1988; 350 pp.
39
[40]. Görener A, Toker K, Uluçay K. Application of combined SWOT and AHP: a case study for a manufacturing firm. Procedia-Social and Behavioral Sciences. 2012; 58: 525-534.
40
[41]. Malczewski J. spatial multi criteria decision analysis In: J. ctill(Ed), Multicriteria decision making and analysis: a geographic information sciences approach. Brook field, VT: Ashgate poblishing; 1999.
41
[42]. Esmaeili R, Jokar E, Roshan neko P. Determination of Flooding potential using TOPSIS method. Physical Geography Research Quarterly. 2016; 31(9): 77-87. [Persian]
42
[43]. Khayri Zadeh M, Maleki J, Hamid A. Flood hazard zonation using ANP model in mardagh chay basin. Quantitative Geomorphology. 2012;1 (3): 39-56. [Persian]
43
[44]. Ahmed F, Srinivasa Rao K. Prioritization of Sub-watersheds based on Morphometric Analysis using Remote Sensing and Geographic Information System Techniques. International Journal of Remote Sensing and GIS, 2015; 4(2): 51-65.
44
[45]. Altaf S, Meraj G, Romshoo S. Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed. Environmental Monitoring and Assessment, 2014; 86(12): 8391-8412.
45
[46]. Dar R, Chandra R, Romshoo S. Morphotectonic and Lithostratigraphic analysis of Intermontane Karewa basin of Kashmir Himalayas, India. Journal of Mountain Science, 2013; 10(1): 1–15.
46
[47]. Strahler AN. Quantitative geomorphology of drainage basins and channel networks. In: Chow, V.T. (Ed.), Handbook of Applied Hydrology. McGraw Hill Book Company, New York. 1964; Section 4-11.
47
[48]. Abedini M, Fathi jokendan R. The zoning of flood Suseptibility in the Gorganrod watershed based on GIS. Hydrogeomorphology. 2016; 7:1-17. [Persian]
48
[49]. Soleimani Sardoo, F. Priority of effective regions on flood peak by using of RS & GIS Techniques and HEC-HMS model at Halilrud, Isfahan University of Technology. Faculty of Natural Resources; 2009.[ In Persian].
49
[50]. Inanlou H. Time and Place priority of flooding in in Kooshak Abad sub watersheds using HEC-HMS model. Master Thesis, Tarbiat Modares University, pp 76. 2006.[ In Persian]
50
[51]. Nasiri Ghidari A, Montazeri AA Momeni M. Ensemble AHP and TOPSIS in determination of relative weights of criteria and assessment of drainage and irrigation networks. Iranian Journal of Irrigation and Drainage. 2010; 4(2): 284-296.[ In Persian]
51
[52]. Hlaing k, Haruyama S, Maung A. Using GIS-based distributed soil loss modeling and morphometric analysis to prioritize watershed for soil conservation in Bago river basin of Lower Myanmar. Front. Earth Science. 2008; 2 (4): 465–478.
52
ORIGINAL_ARTICLE
Regional Flood Hazard assessment at the Sub-basin Scale Using Remote Sensing & Fuzzy logic
Flood is among the most important environmental hazards, broadly threatening human societies and their assets. In this research, by integrating the Rational model to estimate peak runoff into Marand basin flood hazard on a sub-basin scale, assessments are accomplished using remote sensing and GIS. After determining the runoff coefficient land cover/use layers were taken from satellite images of the Sentinel 2A, and the slope map was derived from the ASTER DEM 30m and soil hydrological groups, using the specified amount of rainfall hamely intensity in mode of 1-hour peak runoff was calculated. Using linear membership function in fuzzy logic model, integrating prepared this peak runoff and elevation lines between zero and one were fuzzy and then by applying multiple weight tangles to each of these two layers we collected their results, and the flood hazards distribution map was prepared. With the implementation of prepared risk map in fifth grade the classes include very low-risk, low risk, medium, high and very high risk with the results of GIS partnership or PGIS and entering this information into the confusion matrix. The accuracy of prepared maps was determined to be about 87.83%.
https://ije.ut.ac.ir/article_67508_f70352f656aedf6a04a75ce3e1b32537.pdf
2018-09-23
829
841
10.22059/ije.2018.245661.775
Flood hazard
Sentinel 2A
Fuzzy logic
Marand basin
PGIS
SeidMohamad
Mousavi
mohamad.mousavi368@gmail.com
1
University of Tabriz
AUTHOR
Shahram
Roostaei
roostaei@tabrizu.ac.ir
2
Head of the Faculty of Planning and Environmental Sciences, Tabriz University, Tabriz, Iran.
LEAD_AUTHOR
Hashem
Rostamzadeh
hrostamzadeh@gmail.com
3
Department of Climatology, Faculty of Planning and Environmental Sciences, Tabriz University, Tabriz, Iran
AUTHOR
[1]. Chang LF, Lin CH, Su MD. Application of geographic weighted regression to establish flood-damage functions reflecting spatial variation. Water Sa. 2008 Feb;34(2):209-16.
1
[2]. Ahmadi Ilekhchi, A, Haj Abasi, M, Jalalian, A. The Effect of Rural Areas and Land Use Change on Runoff Production, Journal of Agricultural Science and Natural Resource. 2002; 6; 25-36(Persian).
2
[3]. Malekian, A, Oftadegan khozani, A, Ashourzad, Gh. Flood Hazard Zoning in Watershed Scale using Fuzzy Logic (Case study: Akhtar Abad Watershed). Journal of Natural Geography Research. 2012; 4:44; 131-152 (Persian).
3
[4]. Beroshke, A, Sokouti, R, Montaseri, M, Ghahremani, A, Investigating the phenomenon of flood and its zoning using satellite imagery. Seventh International River Engineering Workshop. University of Shahid Chamran. 2006, pp 8(Persian).
4
[5]. Nyarko BK. Application of a rational model in GIS for flood risk assessment in Accra, Ghana. Journal of Spatial Hydrology. 2002 Jun 7;2(1).
5
[6]. Moel HD, Alphen JV, Aerts JC. Flood maps in Europe-methods, availability and use.
6
[7]. Ghanavati, E, Flood risk zoning in Karaj using fuzzy logic. Geography and environmental hazards. 2013; 8; 113-131(Persian).
7
[8]. Nazmfar, H, Beheshti Javid, A, Fathi, M H, Potential flooding and flood risk zonation using fuzzy logic model (Case study: ghuri chay river catchment). Second International Conference on Environmental Hazards, 7th and 8th of November, Kharazmi Faculty of Tehran, 2013: pp 9(Persian).
8
[9]. Dastourani, M, Hayatzadeh, M, Fathzadeh, A, Hakimzadeh, M A. Investigating the Efficiency of Empirical Relationships in Estimating Flood peak in desert areas of Central Iran. Geography and Development Magazine. 2014; 36; 145-160
9
[10]. Ghanavati, A, Babaei Aghdam, F, Hemmati, T, Rahimi, M. Flood Potential Zoning Using Fuzzy Logic Model in GIS Environment (Case Study of Khayavchi Meshkinshshahr River Basin), Hydrogeomorphology Journal. 2015; 3; 121-135(Persian).
10
[11]. Beheshti Javid, A, Amani, S, Shahi Boyaghchi, M. Assessing the flood potential of the Karnawah River using the fuzzy logic model, the first international congress on land, space and clean energy. 2015: pp7 (Persian).
11
[12]. Sadeghi Goghari, M, Eskandari Damaneh, H, AZareh, A, Flood risk zoning using fuzzy logic. Case study, Isfahan, 7th International Conference on Integrated Management of Crisis, Tehran, Permanent Secretariat of the International Conference on Integrated Management of the Crisis. 2015: pp 10(Persian).
12
[13]. Bhatt GD, Sinha K, Deka PK, Kumar A. Flood hazard and risk assessment in Chamoli District, Uttarakhand using satellite remote sensing and GIS techniques. International Journal of Innovative Research in Science, Engineering and Technology. 2014 Aug;3(8):9.
13
[14]. Asumadu-Sarkodie S, Owusu PA, Jayaweera MP. Flood risk management in Ghana: A case study in Accra. Advances in Applied Science Research. 2015 May 4;6(4):196-201.
14
[15]. Asare-Kyei D, Forkuor G, Venus V. Modeling flood hazard zones at the sub-district level with the rational model integrated with GIS and remote sensing approaches. Water. 2015 Jul 6;7(7):3531-64.
15
[16]. Franci F, Bitelli G, Mandanici E, Hadjimitsis D, Agapiou A. Satellite remote sensing and GIS-based multi-criteria analysis for flood hazard mapping. Natural Hazards. 2016 Oct 1;83(1):31-51.
16
[17]. El Morjani ZE. Methodology document for the WHO e-atlas of disaster risk. Exposure to natural hazards Version. 2011;2.
17
[18]. Bibak, Gh H, Investigating the characteristics of rainfall in Marand city, Geographic Space Magazine. 2008; 8:21; 67-839(Persian).
18
[19]. Almaspour, F, Hafezi Zadeh, S, GIS zoning vulnerability zoning using GIS, remote sensing and multi-criteria evaluation method, a case study of Marand city, the 2nd National Conference on Crisis Management, the role of new technologies in reducing the vulnerability of accidental accidents. 2012: pp 8 (Persian).
19
[20]. Chen M, Su W, Li L, Zhang C, Yue A, Li H. Comparison of pixel-based and object-oriented knowledge-based classification methods using SPOT5 imagery. WSEAS Transactions on Information Science and Applications. 2009 Mar 1;3(6):477-89.
20
[21]. Feizizadeh, B, Jafari, F, Nazmfar, H. Application of Remote Sensing Data in Detection of Land Use Change, Fine Arts Magazine. 2008; 34; pp 20 (Persian).
21
[22]. Mahdavi, M, Applied Hydrology, Vol. 2, Tehran University Press. 2013 (Persian).
22
[23]. Modeling the spatial distribution of lightning and thunder storms using satellite images in the northwest of the country. Master thesis, Department of Natural Geography, Tabriz University. 2007(Persian).
23
[24]. Valizadeh Kamran, Kh, Nasiri Ghaleh Bin, S, Investigation of Thunderstorm Rainfall in the Highlands of the Northwest of Iran, First National Conference on Geography, Urban Development and Sustainable Development, Tehran, Koomesh Environmental Society, Aviation University. 2013 (Persian).
24
[25]. Alizadeh, A, Principle of Applied Hydrology. Ferdowsi University of Mashhad Press, Iran, 2015, 650 (Persian).
25
[26]. Investigating the effect of topographic factor on the spatial distribution of precipitation using interpolation methods in Tehran, 1st International Congress of Land, Clean Space and Energy, Ardebil, Mohaghegh Ardebil University. 2015(Persian).
26
[27]. Viessman, W.J., Lewis, G.L. Introduction to Hydrology. New York: Harper Collins College Publishers; 1996.
27
[28]. County K. Knox County Tennessee Stormwater Management Manual. Knox County, Knoxville, TN. 2008. Available online: http://www.knoxcounty.org/stormwater/pdfs/vol2/3-1-3%20Rational%20Method.pdf (accessed on 15 July 2014)
28
[29]. Momeni M. New Operational Research Topics. 2nd ed. Tehran: University of Tehran Faculty of Management; 2008(Persian)
29
[30]. Malczewski J. GIS and multicriteria decision analysis. John Wiley & Sons; 1999 Apr 5.
30
[31]. Malczewski J. On the use of weighted linear combination method in GIS: common and best practice approaches. Transactions in GIS. 2000 Jan 1;4(1):5-22.
31
ORIGINAL_ARTICLE
Determining of the Oak forests’ role on protecting water quality based on the service function in Tang-e Shool, Fars
Surface water is an important source for drinking water, which are under environmental and anthropogenic stresses. There are several evidences confirming that forest cover positively affects water quality. However, little quantitate information is available regarding the impacts of forest canopy cover on purification of drinking water quality in Iran. To achieve this, we selected several forested catchments with different ground canopy cover in Tang-e-Shool forest located in the Fars province and Water Quality Index (WQI) were measured by using physicochemical parameters. The production function of water quality service was also estimated by taking into account other characteristics of the forested micro catchments. Our results showed that the WQI in 30% crown cover is in a good quality scale. Also with an increase of 1% in the crown cover, WQI will improve by 0.8% and an increase of 1% in the area of the micro catchment causes a decline of 0.14 percent of WQI. Also, Aghajari Formation has a negative effect on WQI compare to Asmari. According to this study increasing the crown cover to protect water quality by up to 30%, will minimize treatment costs and the waters provided by these forests can be used for human drinking purposes.
https://ije.ut.ac.ir/article_67553_d54821402e49d700b07bfe8aff9e0b93.pdf
2018-09-23
843
853
10.22059/ije.2018.249562.800
oak forests
Water quality index
physicochemical parameters
crown cover percentage
Touba
Rousta
rousta.t@ut.ac.ir
1
Department of forestry and forest economy, faculty of natural resources, university of Tehran, Karaj, Iran
AUTHOR
Ali akbar
Nazari samani
aknazari@ut.ac.ir
2
department of reclamation of arid region, faculty of natural resources, university of Tehran, Karaj
AUTHOR
Seyed Mahdi
Heshmatolvaezin
mheshmat@ut.ac.ir
3
Department of forestry and forest economy, faculty of natural resources, university of Tehran, Karaj
AUTHOR
Mansour
Zibaei
zibaei@shirazu.ac.ir
4
Department of Agricultural Economics, Faculty of Agriculture, University of Shiraz, Shiraz
AUTHOR
Pedram
Attarod
attarod@ut.ac.ir
5
Department of forestry, Faculty of Natural Resources, University of Tehran, Karaj
AUTHOR
Seyed Kazem
Bordbar
sbordbar86@gmail.com
6
Fars Agriculture and Natural Resources Researches Center, Shiraz
AUTHOR
[1]. MEA (Millennium Ecosystem Assessment (Program)). Ecosystems and Human Well-Being: Synthesis; Washington. Island. DC, USA, 2005.
1
[2]. Melissa M K, Damian C A, Francisco J E. The Value of Forest Conservation for Water Quality Protection, Forests 2014; 5:862-884.
2
[3]. Hong B, Limburg K E, Erickson J D, Gowdy J M, Nowosielski A A, Polimeni J M, & Stainbrook K M. Connecting the ecological-economic dots in human-dominated watersheds: Models to link socio-economic activities on the landscape to stream ecosystem health. Landscape and Urban Planning. 2009; 91(2): 78-87.
3
[4]. Dudley N, Stolton S. Running Pure: The importance of forest protected areas to drinking water Arguments for Protection: World Bank/WWF Alliance for Forest Conservation and Sustainable Use 2003.
4
[5]. Daisy N, Laura N, Carlos O. Forest and water: The Value of native temperature forest in supplying water for human consumption, Ecological Economics. 2006; 58: 606-612.
5
[6]. Warziniack T, Sham CH, Morgan R, Feferholtz Y. Effect of forest cover on water treatment costs. American Water Works Association. Rocky Mountain Research Station. 2016.
6
[7]. Fiquepron J, Garcia S, Stanger A. Land use impact on water quality: Valuing forest services in terms of the water supply sector. Journal of Environmental Management. 2013;126:113-121.
7
[8]. Etehadi Abari M. The effect of forest cover induced by harvesting scenarios on runoff quantity, quality and sediment yield in Kheirud Forest. A thesis submitted to the graduate studies office in partial fulfilment of the requirement for the doctor philosophy in forest engineering. University of Tehran.2017. [Persian].
8
[9]. Abildtrup J, Garcia S, Stanger A. The effect of forest land use on the cost of drinking water supply
9
A spatial econometric analysis. Paper prepared for presentation at the EAAE 2011 Congress Change and Uncertainty Challenges for Agriculture, Food and Natural Resources. ETH Zurich, Switzerland 2011.
10
[10]. Freeman D, Madsen R, Har K. Statistical analysis of drinking water treatment plant costs, course water quality, and land cover characteristics. 2008; Trust for Public Land
11
[11]. Ernst C, Gullick R, Nixon, K. Protecting the source e conserving forests to protect water. American Water Works Association 2004; 30 (5): 3-7.
12
[12]. Brogna D, Michez A, Jacobs S, Dufrêne M, Vincke C, Dendonker N. Linking Forest Cover to Water Quality: A Multivariate Analysis of Large Monitoring Datasets. Water. 2017; 9:176-193.
13
[13]. Ramesh S, Sukumaran N, Murugesan AG, Rajan MP. An innovative approach of drinking water quality index- A case study from Southern Tamil Nadu, India. Ecological Indicators. 2010; 10(4):857-68
14
[14]. Cude CG. Oregon water quality index a tool for evaluating water quality management effectiveness1. Journal of American Water Resource Association. 2001; 37(1): 125-137.
15
[15]. Beckman B, Bodis D, Lahermo P, Rapant S, Tarvainen T. Application of groundwater contamination index in Finland and Slovakia. Environ Geol 1998; 36: 55-64. Doi: 1007. S002540050320.
16
[16]. Brown RM, McClelland N I, Deininger R A, Tozer R G. A water quality index: do we dare? Water Seage Works. 1970; 117: 339-343.
17
[17]. Canter L W. “Environmental Impact Assessment,” 2nd Edition, McGraw-Hill Inc. New York, USA, 1996.
18
[18]. Abdul Hameed M. Alobaidy J, Bahram K. Maulood, Abass J. Evaluating Raw and Treated Water Quality of Tigris River within Baghdad by Index Analysis. J. Water Resource and Protection, 2010; 2: 629-635.
19
[19]. Sener S, Sener E, Davras A. Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey). Science of The Total Environment. 2017; 584-585: 131-144
20
[20]. Wu Z, Wang X, Chen Y, Cai Y, Deng J. Assessing river water quality using water quality index in Lake Taihu Basin, China. Science of The Total Environment. 2017; 612(1): 914-922.
21
[21]. Yousefi H, Zahedi S, Niksokhan MH, Modifying the analysis made by water quality index using multi-criteria decision making methods. Journal of African Earth Sciences. 2017; 138(1): 309-318.
22
[22]. Cosultant engineers of soil and water researchers. Revision of studies and analysis of statistics and information in the paired catchments in Tang-e Shool. [2015]. [Persian].
23
[23]. Sheikhi Almanabad Z, Asadzadeh F, Pirkharati H. Application of the DWQI Index for Comprehensive Quality Assessment in Ardebil water table.Ecohydrology. 2017; 4(2): 421-436. [Persian].
24
[24]. World Health Organization. Guidelines For Drinking Water Quality. Second addendum. Vol. 1, Recommendations. 3rd ed. ISBN 978 92 4 154760 4. 2008; World Health Organization.
25
[25]. Institute of Standards and Industrial Research of Iran. Drinking water - Physical and chemical specifications.ISIRI, 1053. 2008; 5th Revision [Persian].
26
[26]. Yogendra K, Pouttaiah E T. Determination of water quality Index and Suitability of an Urban Waterbody in Shimoga Town. Karantaka. The 12th Word Lake Conference, 2008; 342-346.
27
[27]. Chatterji C, Raziuddin M. Determination of water quality index (WQI) of a degraded river in Asanol Industrial area, Raniging, Burdwan, West Bengal. Nature, Environmental and pollution Technology. 2002; 1(2):181-189.
28
[28]. Kohi Kamali M, Rajabi MA. Determining the Effect of Urban Green Space on Residential Utility Value. Journal of GIS, RS application in programming. 2010; 1(1): 23-31.
29
[29]. Mahdavi, M. editor. Applied Hydrology, 2125. 7th Edition. Tehran. University of Tehran. 2011. p.427- 302. [Persian].
30
[30]. Noori Z. Malekian A. The Effective Factors on Water Quality of Seimareh and Kashkan Rivers in Ilam and LorestanProvinces. Natural Environment. Iranian Natural Resources Journal. 2015: 69(2): 549-564. [Persian].
31
[31]. Zhenyao Sh. Lei Ch. Qian L. Ruimin L. Qian H. Impact of spatial rainfall variability on hydrology and nonpoint source pollution modeling. Journal of Hydrology. 2012: 427-437, 205-215.
32
[32]. Ahmed L M. Helal A A. Naema A EL. Gamal R. Nahel O. Shaker N O. Helal A A. Influence of some organic ligands on the adsorption of lead by agricultural soil. Arabian Journal of Chemistry 2015. Articlr in Press.
33
[33]. Allen M F. Swenson 1 W. 1 Querejeta J I.. Egerton-Warburton L M. Treseder KK. ECOLOGY OF MYCORRHIZAE: A Conceptual Framework for Complex Interactions Among Plants and Fung. Annu. Rev. Phytopathol. 2003. 41:271–303 .
34
ORIGINAL_ARTICLE
A review of methods for removing heavy metal from aqueous media
The present paper briefly describes heavy metals from various aspects, the sources of production and the negative effects of these metals on human health, the standards and regulations of various agencies to comply with the permissible limits and finally, it also explores the methods of purification, separation and removal of these elements. Regarding treatment methods, a wide range of processes and disadvantages and advantages of each of the old types to the new technologies has been investigated, including chemical precipitation, coagulation-flocculation, flotation, ion exchange, electrochemical treatment, membrane filtration and adsorption. In this regard, absorption is considered as a simple but efficient approach, and has been specifically addressed and sorbents have been studied including active carbon, carbon nanotubes, graphene oxides and biosorbents. Today, nanoscale materials have shown significant applications in the treatment of aquatic environments, due to their unique features such as high surface area, large active sites and high absorption capacity. In this regard, magnetic nanoparticles of iron oxides are considered as cost-effective, high-efficiency and environmentally friendly adsorbents.
https://ije.ut.ac.ir/article_67554_bf04c957eacc31d6edf252fe043edaa3.pdf
2018-09-23
855
874
10.22059/ije.2018.249854.804
Removal methods
Heavy metals
aqueous media
Adsorption
Magnetic nanoparticles
Mohamad Hosein
Fatehi
mhfd63@yahoo.com
1
Chemical and Petroleum Engineering Department, Sharif University of Technology, Tehran, Iran.
LEAD_AUTHOR
Jalal
Shayegan
shayegan@sharif.edu
2
Chemical and Petroleum Engineering Department, Sharif University of Technology, Tehran, Iran.
AUTHOR
Mohamad
Zabihi
zabihi@sut.ac.ir
3
Chemical Engineering Department, Sahand University of Technology, Tabriz, Iran.
AUTHOR
[1]. IUPAC Technical Report, “Heavy Metals”— A Meaningless Term? Pure and Applied Chemistry. 2002; 74(5): 793–807.
1
[2]. U.S. Environmental Protection Agency. EPA’s Terms of Environment, 2000.
2
[3]. World Health Organization. Adverse Health effects of Heavy Metals in Children, 2011.
3
[4]. Chowdhury S, Jafar Mazumder M.A, Al-Attas O, Husain T. Heavy metals in drinking water: Occurrences, implications, and future needs in developing countries. Science of the Total Environment. 2016; 569-570: 476-488.
4
[5]. Bruins M.R, Kapil S, Oehme F.W. Microbial Resistance to Metals in the Environment. Ecotoxicology and Environmental Safety. 2000; 45: 198-207.
5
[6]. Hussein H, Farag S, Kandil K, Moawad H. Tolerance and uptake of heavy metals by Pseudomonads. Process Biochemistry. 2005; 40: 955–961.
6
[7]. Nies D.H. Microbial heavy-metal resistance. Applied Microbiology Biotechnology. 1999; 51: 730-750.
7
[8]. Song J, Kong H, Jang J. Adsorption of heavy metal ions from aqueous solution by polyrhodanine-encapsulated magnetic nanoparticles. Journal of Colloid and Interface Science. 2011; 359: 505–511.
8
[9]. Geiger A, Cooper J. Overview of Airborne Metals Regulations, Exposure Limits, Health Effects, and Contemporary Research. Cooper Environmental Services LLC. 2010.
9
[10]. Li H, Xiao D, He H, Lin R, Zuo P. Adsorption behavior and adsorption mechanism of Cu(II) ions on amino-functionalized magnetic nanoparticles. Transactions of Nonferrous Metals Society of China. 2013; 23: 2657−2665.
10
[11]. Mohan D, Pittman Jr C.U. Arsenic removal from water/wastewater using adsorbents - A critical review. Journal of Hazardous Materials. 2007; 142: 1–53.
11
[12]. Mandal B.K, Suzuki K.T. Arsenic round the world: a review. Talanta. 2002; 58: 201–235.
12
[13]. Huang J, Yuan F, Zeng G, Li X, Gu Y, Shi L, Liu W, Shi Y. Influence of pH on heavy metal speciation and removal from wastewater using micellar-enhanced ultrafiltration. Chemosphere. 2017; 173: 199-206.
13
[14]. Badruddoza A.Z.M, Shawon Z.B.Z, Daniel T.W.J, Hidajat K, Shahab Uddin M. Fe3O4/cyclodextrin polymer nanocomposites for selective heavy metals removal from industrial wastewater. Carbohydrate Polymers. 2013; 91: 322–332.
14
[15]. Xia Z, Baird L, Zimmerman N, Yeager M. Heavy metal ion removal by thiol functionalized aluminum oxidehydroxide nanowhiskers. Applied Surface Science. 2017; 416: 565–573.
15
[16]. Whitacre D.M. Reviews of Environmental Contamination and Toxicology, Vol 224. Springer; 2013.
16
[17]. Fowler B.A. Monitoring of human populations for early markers of cadmium toxicity: A review. Toxicology and Applied Pharmacology. 2009; 238: 294–300.
17
[18]. Aoshima K. Itai-itai disease: Renal tubular osteomalacia induced by environmental exposure to cadmium—historical review and perspectives. Soil Science and Plant Nutrition. 2016; 62(4): 319-326.
18
[19]. Costa M, Klein C.B. Toxicity and Carcinogenicity of Chromium Compounds in Humans. Critical Reviews in Toxicology. 2006; 36(2): 155-163.
19
[20]. Patlolla A.K, Armstrong N, Tchounwou P.B. Cytogenetic evaluation of potassium dichromate toxicity in bone marrow cells of Sprague-Dawley rats. Metal Ions in Biology and Medicine. 2008; 10: 353–358.
20
[21]. Luch A. Molecular, Clinical and Environmental Toxicology - Volume 3: Environmental Toxicology. Springer; 2012.
21
[22]. Broadley M.R, White P.J, Hammond J.P, Zelko I, Lux A. Zinc in plants. New Phytologist. 2007; 173(4): 677-702.
22
[23]. Santamaria A.B. Manganese exposure, essentiality & toxicity. Indian Journal of Medical Research. 2008; 128: 484-500.
23
[24]. Cempel M, Nikel G. Nickel: A Review of Its Sources and Environmental Toxicology. Polish Journal of Environmental Studies. 2006; 15(3): 375-382.
24
[25]. Denkhaus E, Salnikow K. Nickel essentiality, toxicity, and carcinogenicity. Critical Reviews in Oncology/Hematology. 2002; 42: 35–56.
25
[26]. Selinus O. Essentials of Medical Geology. Springer; 2013.
26
[27]. Sun H.J, Rathinasabapathi B, Wu B, Luo J, Pu L.P, Ma L.Q. Arsenic and selenium toxicity and their interactive effects in humans. Environment International. 2014; 69: 148–158.
27
[28]. Tchounwou P.B, Ayensu W.k, Ninashvilli N, Sutton D. Environmental exposures to mercury and its toxicopathologic implications for public health. Environmental Toxicology. 2003; 18:149–175.
28
[29]. Rooney J.P.K. The role of thiols, dithiols, nutritional factors and interacting ligands in the toxicology of mercury. Toxicology. 2007; 234: 145–156.
29
[30]. Dopp E, Hartmann L.M, Florea A.M, Rettenmier A.W, Hirner A.V. Environmental distribution, analysis, and toxicity of organometal (loid) compounds. Critical Reviews in Toxicology. 2004; 34: 301–333.
30
[31]. Brewer G.J. Risks of Copper and Iron Toxicity during Aging in Humans. Chemical Research in Toxicology. 2010; 23: 319-326.
31
[32]. Gaetke L.M, Chow C.K. Copper toxicity, oxidative stress, and antioxidant nutrients. Toxicology. 2003; 189: 147-163.
32
[33]. Genderen E.J.V, Ryan A.C, Tomasso J.R, Klaine S.J. Evaluation of acute copper toxicity to larval fathead minnows (PIMEPHALES PROMELAS) in soft surface waters. Environmental Toxicology and Chemistry. 2005; 24: 408-414.
33
[34]. Agency for Toxic Substances and Disease Registry. Toxicological Profile for Cobalt. U.S. Department of Health and Human Services, Atlanta. 2004.
34
[35]. Simonsen L.O, Harbak H, Bennekou P. Cobalt metabolism and toxicology - A brief update. Science of the Total Environment. 2012; 432: 210–215.
35
[36]. US Environmental Protection Agency (EPA), ed. US EPA, Washington DC, vol. EPA832-F-00-018; 2000.
36
[37]. Duruibe J.O, Ogwuegbu M.O.C, Egwurugwu J.N. Heavy metal pollution and human biotoxic effects. International Journal of Physical Sciences. 2007; 2(5): 112-118.
37
[38]. Food and Agriculture Organization of the United Nations. Water Quality for Agriculture, Irrigation and Drainage Paper No. 29, Rev. 1., Rome; 1985.
38
[39]. Hasanzadeh R, Moghadam P.N, Bahri-Laleh N, Sillanpaa M. Effective removal of toxic metal ions from aqueous solutions: 2-Bifunctional magnetic nanocomposite base on novel reactive PGMA-MAn copolymer@Fe3O4 nanoparticles. Journal of Colloid and Interface Science. 2017; 490: 727–746.
39
[40]. Valls M, de Lorenzo V, Gonzalez-Duarte R, Atrian S. Engineering outer-membrane proteins in Pseudomonas putida for enhanced heavy-metal bioadsorption. Journal of Inorganic Biochemistry. 2000; 79: 219–223.
40
[41]. Fan H.L, Zhou S.F, Jiao W.Z, Qi G.S, Liu Y.Z. Removal of heavy metal ions by magnetic chitosan nanoparticles prepared continuously via high-gravity reactive precipitation method. Carbohydrate Polymers. 2017; 174: 1192–1200.
41
[42]. Benefield L.D, Morgan J.M. Chemical precipitation, in: R.D. Letterman (Ed.). Water Quality and Treatment. NY: McGraw-Hill Inc; 1999.
42
[43]. Wang L.K, Vaccari D.A, Li Y, Shammas N.K. Chemical precipitation, in: L.K. Wang, Y.T. Hung, N.K. Shammas (Eds.), Physicochemical Treatment Processes, vol. 3. NJ: Humana Press; 2004.
43
[44]. Juttner K, Galla U, Schmieder H. Electrochemical approaches to environmental problems in the process industry. Electrochimica Acta. 2000; 45: 2575–2594.
44
[45]. Yang X.J, Fane A.G, MacNaughton S. Removal and recovery of heavy metals from wastewaters by supported liquid membranes. Water Science and Technology. 2001; 43(2): 341–348.
45
[46]. Azimi A, Azari A, Rezakazemi M, Ansarpour M. Removal of Heavy Metals from Industrial Wastewaters: A Review. ChemBioEng Reviews. 2017; 4(1): 1–24.
46
[47]. Shammas N.K. Coagulation and flocculation, in: L.K. Wang, Y.T. Hung, N.K. Shammas (Eds.), Physicochemical Treatment Processes, vol. 3. NJ: Humana Press; 2004.
47
[48]. Semerjian L, Ayoub G.M. High-pH-magnesium coagulation–flocculation in wastewater treatment. Advances in Environmental Research. 2003; 7: 389–403.
48
[49]. Licsk´o I. Realistic coagulation mechanisms in the use of aluminium and iron(III) salts. Water Science and Technology. 1997; 36(4): 103–110.
49
[50]. Charerntanyarak L. Heavy metals removal by chemical coagulation and precipitation. Water Science and Technology. 1999; 39(10/11): 135–138.
50
[51]. Ayoub G.M, Semerjian L, Acra A, El Fadel M, Koopman B. Heavy metal removal by coagulation with seawater liquid bittern. Journal of Environmental Engineering. 2001; 127(3): 196–202.
51
[52]. Wang L.K, Fahey E.M, Wu Z.C. Dissolved air flotation, in: L.K. Wang, Y.T. Hung, N.K. Shammas (Eds.), Physicochemical Treatment Processes, vol. 3. NJ: Humana Press; 2004.
52
[53]. Zabel T. Flotation in water treatment, in: K.J. Ives (Ed.), The Scientific Basis of Flotation. The Hague: Martinus Nijhoff Publishers; 1984.
53
[54]. Jokela P, Keskitalo P. Plywood mill water system closure by dissolved air flotation treatment. Water Science and Technology. 1999; 40(11/12): 33–42.
54
[55]. Matis K.A, Zouboulis A.I, Gallios G.P, Erwe T, Bl¨ocher C. Application of flotation for the separation of metal-loaded zeolite. Chemosphere. 2004; 55: 65–72.
55
[56]. Taseidifar M, Makavipour F, Pashley R.M, Mokhlesur Rahman A.F.M. Removal of heavy metal ions from water using ion flotation. Environmental Technology & Innovation. 2017; 8: 182–190.
56
[57]. Dabrowski A, Hubicki Z, Podkoscielny P, Robens E. Selective removal of the heavy metal ions from waters and industrial wastewaters by ion-exchange method. Chemosphere. 2004; 56: 91–106.
57
[58]. Alyüz B, Veli S. Kinetics and equilibrium studies for the removal of nickel and zinc from aqueous solutions by ion exchange resins. Journal of Hazardous Materials. 2009; 167: 482-488.
58
[59]. Rengaraj S, Kyeong-Ho Y, Seung-Hyeon M. Removal of chromium from water and wastewater by ion exchange resins. Journal of Hazardous Materials. 2001; B87: 273–287.
59
[60]. Ahmed S, Chughtai S, Keane M.A. The removal of cadmium and lead from aqueous solution by ion exchange with Na–Y zeolite. Separation and Purification Technology. 1998; 13: 57–64.
60
[61]. Tran T.K, Chiu K.F, Lin C.Y, Leu H.J. Electrochemical treatment of wastewater: Selectivity of the heavy metals removal process. International Journal of Hydrogen Energy. 2017; 42(45): 27741-27748.
61
[62]. Wang L.K, Hung Y.T, Shammas N.K. Advanced physicochemical treatment technologies, In: Handbook of Environmental Engineering, vol. 5. NJ: Humana; 2007.
62
[63]. Chen G. Electrochemical technologies in wastewater treatment. Separation and Purification Technology. 2004; 38: 11-41.
63
[64]. Heidmann I, Calmano W. Removal of Zn(II), Cu(II), Ni(II), Ag(I) and Cr(VI) present in aqueous solutions by aluminium electrocoagulation. Journal of Hazardous Materials. 2008; 152: 934–941.
64
[65]. Gherasim C.V, Krivcik J, Mikulasek P. Investigation of batch electrodialysis process for removal of lead ions from aqueous solutions. Chemical Engineering Journal. 2014; 256: 324–334.
65
[66]. Lu H, Wang Y, Wang J. Recovery of Ni2+ and pure water from electroplating rinse wastewater by an integrated two-stage electrodeionization process. Journal of Cleaner Production. 2015; 92: 257-266.
66
[67]. Saffaj N, Loukili H, Younssi S.A, Albizane A, Bouhria M, Persin M, Larbot A. Filtration of solution containing heavy metals and dyes by means of ultrafiltration membranes deposited on support made of Moroccan clay. Desalination. 2004; 168: 301–306.
67
[68]. Landaburu-Aguirre J, García V, Pongrácz E, Keiski R.L. The removal of zinc from synthetic wastewaters by micellar-enhanced ultrafiltration: statistical design of experiments. Desalination. 2009; 240: 262-269.
68
[69]. Huang J.H, Zeng G.M, Zhou C.F, Li X, Shi L.J, He S.B. Adsorption of surfactant micelles and Cd2+/Zn2+ in micellar-enhanced ultrafiltration. Journal of Hazardous Materials. 2010; 183: 287-293.
69
[70]. Barakat M.A, Schmidt E. Polymer-enhanced ultrafiltration process for heavy metals removal from industrial wastewater. Desalination. 2010; 256: 90-93.
70
[71]. Shahalam A.M, Al-Harthyb A, Al-Zawhryb A. Feed water pretreatment in RO systems: unit processes in the Middle East. Desalination. 2002; 150: 235-245.
71
[72]. Ujang Z, Anderson G.K. Application of low-pressure reverse osmosis membrane for Zn2+ and Cu2+ removal from wastewater. Water Science Technology. 1996; 34(9): 247–253.
72
[73]. Potts D.E, Ahlert R.C, Wang S.S. A critical review of fouling of reverse osmosis membranes. Desalination. 1981; 36: 235–264.
73
[74]. Ning R.Y. Arsenic removal by reverse osmosis. Desalination. 2002; 143: 237–241.
74
[75]. Slater C.S, Ahlert R.C, Uchrin C.G. Applications of reverse osmosis to complex industrial wastewater treatment. Desalination. 1983; 48: 171–187.
75
[76]. Alvarez-Vazquez H, Jefferson B, Judd S.J. Membrane bioreactors vs. conventional biological treatment of landfill leachate: a brief review. Journal of Chemical Technology and Biotechnology. 2004; 79: 1043–1049.
76
[77]. Al-Rashdi B, Somerfield C, Hilal N. Heavy Metals Removal Using Adsorption and Nanofiltration Techniques. Separation & Purification Reviews. 2011; 40(3): 209-259.
77
[78]. Kotrappanavar N.S, Hussain A.A, Abashar M.E.E, Al-Mutaz I.S, Aminabhavi T.M, Nadagouda M.N. Prediction of physical properties of nanofiltration membranes for neutral and charged solutes. Desalination. 2011; 280: 174–182.
78
[79]. Al-Rashdi B.A.M, Johnson D.J, Hilal N. Removal of heavy metal ions by nanofiltration. Desalination. 2013; 315: 2–17.
79
[80]. Madaeni S.S, Mansourpanah Y. COD removal from concentrated wastewater using membranes. Filtration & Separation. 2003; 40: 40–46.
80
[81]. Ojedokun A.T, Bello O.S. Sequestering heavy metals from wastewater using cow dung. Water Resources and Industry. 2016; 13: 7–13.
81
[82]. Demirbas A. Heavy metal adsorption onto agro-based waste materials: a review. Journal of Hazardous Materials. 2008; 157: 220–229.
82
[83]. Ahmadpour A, Zabihi M, Tahmasbi M, Rohani Bastami T. Effect of adsorbents and chemical treatments on the removal of strontium from aqueous solutions. Journal of Hazardous Materials. 2010; 182: 552-556.
83
[84]. Ahmadpour A, Rohani Bastami T, Tahmasbi M, Zabihi M. Rapid removal of heavy metals ions from aqueous solutions by low cost adsorbents. International Journal of Global Environmental Issues. 2012; 12: 318-331.
84
[85]. Vunain E, Mishra A.K, Mamba B.B. Dendrimers, mesoporous silicas and chitosan-based nanosorbents for the removal of heavy-metal ions: a review. International Journal of Biological Macromolecules. 2016; 86: 570–586.
85
[86]. Markovic S, Stankovic A, Lopicic Z, Lazarevic S, Stojanovic M, Uskokovic D. Application of raw peach shell particles for removal of methylene blue. Journal of Environmental Chemical Engineering. 2015; 3: 716-724.
86
[87]. Mezohegyi G, van der Zee F.P, Font J, Fortuny A, Fabregat A. Towards advanced aqueous dye removal processes: a short review on the versatile role of activated carbon. Journal of Environmental Management. 2012; 102: 148–164.
87
[88]. Zabihi M, Ahmadpour A, Asl A.H. Removal of mercury from water by carbonaceous sorbents derived from walnut shell. Journal of Hazardous Materials. 2009; 167: 230–236.
88
[89]. Zabihi M, Asl A.H, Ahmadpour A. Studies on adsorption of mercury from aqueous solution on activated carbons prepared from walnut shell. Journal of Hazardous Materials. 2010; 174: 251–256.
89
[90]. Tounsadi H, Khalidi A, Machrouhi A, Farnane M, Elmoubarki R, Elhalil A, Sadiq M, Barka N. Highly efficient activated carbon from Glebionis coronaria L. biomass: optimization of preparation conditions and heavy metals removal using experimental design approach. Journal of Environmental Chemical Engineering. 2016; 4: 4549–4564.
90
[91]. Zhang L, Zeng Y, Cheng Z. Removal of heavy metal ions using chitosan and modified chitosan: A review. Journal of Molecular Liquids. 2016; 214: 175-191.
91
[92]. Fu F, Wang Q. Removal of heavy metal ions from wastewaters: a review. Journal of Environmental Management. 2011; 92: 407–418.
92
[93]. Ihsanullah, Al-Khaldi F.A, Abu-Sharkh B, Abulkibash A.M, Qureshi M.I, Laoui T, Atieh M.A. Effect of acid modification on adsorption of hexavalent chromium (Cr(VI)) from aqueous solution by activated carbon and carbon nanotubes. Desalination and Water Treatment. 2015; 57: 7232–7244.
93
[94]. Sun W.L, Xia J, Shan Y.C. Comparison kinetics studies of Cu(II) adsorption by multi-walled carbon nanotubes in homo and heterogeneous systems: Effect of nano-SiO2. Chemical Engineering Journal. 2014; 250: 119–127.
94
[95]. Peng W, Li H, Liu Y, Song S. A review on heavy metal ions adsorption from water by graphene oxide and its composites. Journal of Molecular Liquids. 2017; 230: 496–504.
95
[96]. Babel S, Kurniawan T.A. Low-cost adsorbents for heavy metals uptake from contaminated water: a review. Journal of Hazardous Materials. 2013; B97: 219–243.
96
[97]. Volesky B. Detoxification of metal-bearing effluents: biosorption for the next century. Hydrometallurgy. 2001; 59: 203–216.
97
[98]. Chojnacka K. Biosorption and bioaccumulation—the prospects for practical applications. Environment International. 2010; 36: 299–307.
98
[99]. Wang J, Chen C. Biosorbents for heavy metals removal and their future. Biotechnology Advances. 2009; 27: 195–226.
99
[100]. Vijayaraghavan K, Yun Y.S. Bacterial biosorbents and biosorption. Biotechnology Advances. 2008; 26: 266–291.
100
[101]. Cabuk A, Iulhan S, Filik C, Caliskan F. Pb2+ biosorption by pretreated fungal biomass. Turkish Journal of Biology. 2005; 29: 23–28.
101
[102]. Gerbino E, Carasi P, Araujo-Andrade C, Elizabeth Tymczyszyn E, Gomez-Zavaglia A. Role of S-layer proteins in the biosorption capacity of lead by Lactobacillus kefir. World Journal of Microbiology and Biotechnology. 2015; 31: 583–592.
102
[103]. Srinath T, Verma T, Ramteke P.W, Garg S.K. Chromium (VI) biosorption and bioaccumulation by chromate resistant bacteria. Chemosphere. 2002; 48: 427–435.
103
[104]. Ahluwalia S.S, Goyal D. Microbial and plant derived biomass for removal of heavy metals from wastewater. Bioresource Technology. 2007; 98: 2243–2257.
104
[105]. Yilmazer P, Saracoglu N. Bioaccumulation and biosorption of copper (II) and chromium (III) from aqueous solutions by Pichia stiptis yeast. Journal of Chemical Technology and Biotechnology. 2009; 84: 604–610.
105
[106]. Kocberber N, Donmez G. Chromium (VI) bioaccumulation capacities of adapted mixed cultures isolated from industrial saline wastewaters. Bioresource Technology. 2007; 98: 2178–2183.
106
[107]. Malik A. Metal bioremediation through growing cells. Environment International. 2004; 30: 261– 278.
107
[108]. Dhankhar R, Hooda A. Fungal biosorption—an alternative to meet the challenges of heavy metal pollution in aqueous solutions. Environmental Technology. 2011; 32: 467–491.
108
[109]. Attia T.M.S, Hu X.L, Yin D.Q. Synthesised magnetic nanoparticles coated zeolite (MNCZ) for the removal of arsenic (As) from aqueous solution. Journal of Experimental Nanoscience. 2014; 9: 551-560.
109
[110]. Chan H.B.S, Ellis B.L. Carbon-encapsulated radioactive 99mTc nanoparticles. Advanced Materials. 2004; 16: 144–9.
110
[111]. Dias A.M.G.C, Hussain A, Marcos A.S, Roque A.C.A. A biotechnological perspective on the application of iron oxide magnetic colloids modified with polysaccharides. Biotechnology Advances. 2011; 29: 142–155.
111
[112]. Fatehi M.H, Shayegan J, Zabihi M, Goodarznia I. Functionalized magnetic nanoparticles supported on activated carbon for adsorption of Pb (II) and Cr(VI) ions from saline solutions. Journal of Environmental Chemical Engineering. 2017; 5: 1754-1762.
112
[113]. Fan F.L, Qin Z, Bai J, Rong W.D, Fan F.Y, Tian W, Wu X.L, Wang Y, Zhao L. Rapid removal of uranium from aqueous solutions using magnetic Fe3O4–SiO2 composite particles. Journal of Environmental Radioactivity. 2012; 106: 40–46.
113
[114]. Morcos T.N, Shafik S.S, Ghoniemy H.F. Self-diffusion of cesium ions in hydrous manganese dioxide from mixed solvent solutions. Solid State Ionics. 2003; 167: 431–436.
114
[115]. Lin C.L, Lee C.F, Chiu W.Y. Preparation and properties of poly (acrylic acid) oligomer stabilized super paramagnetic ferro fluid. Journal of Colloid and Interface Science. 2005; 291: 411–420.
115
[116]. Feng Y, Gong J.L, Zeng G.M, Niu Q.Y, Zhang H.Y, Niu C.G, et al. Adsorption of Cd (II) and Zn (II) from aqueous solutions using magnetic hydroxyapatite nanoparticles as adsorbents. Chemical Engineering Journal. 2010; 162(2): 487–494.
116
[117]. Sung Y.K, Ahn B.W, Kang T.J. Magnetic nano fibers with core (Fe3O4 nanoparticle suspension)/sheath (poly ethylene terephthalate) structure fabricated by coaxial electro spinning. Journal of Magnetism and Magnetic Materials. 2012; 324: 916–922.
117
[118]. Jeong U, Teng X, Wang Y, Yang H, Xia Y. Superparamagnetic colloids: controlled synthesis and niche applications. Advanced Materials. 2007; 19: 33–60.
118
[119]. Girginova P.I, Daniel-da-Silva A.L, Lopes C.B, Figueira P, Otero M, Amara V.S, et al. Silica coated magnetite particles for magnetic removal of Hg2+ from water. Journal of Colloid and Interface Science. 2010; 345(2): 234–40.
119
[120]. Mahmoudi M, Sant S, Wang B, Laurent S, Sen T. Superparamagnetic iron oxide nanoparticles (SPIONs): development, surface modification and applications in chemotherapy. Advanced Drug Delivery Reviews. 2011; 63: 24–46.
120
[121]. Laurent S, Forge D, Port M, Roch A, Robic C, Vander Elst L, et al. Magnetic iron oxide nanoparticles: synthesis, stabilization, vectorization, physicochemical characterizations, and biological applications. Chemical Reviews. 2008; 108(6): 2064–2110.
121
[122]. Teja A.S, Koh P.Y. Synthesis, properties, and applications of magnetic iron oxide nanoparticles. Progress in Crystal Growth and Characterization of Materials. 2009; 55(1–2): 22–45.
122
[123]. Hu H, Wang Z, Pan L. Synthesis of monodisperse Fe3O4–silica core–shell microspheres and their application for removal of heavy metal ions from water. Journal of Alloys and Compounds. 2010; 492(1–2): 656–661.
123
ORIGINAL_ARTICLE
Nitrate removal of water and wastewater by solid phase denitrification
Years ago nitrate pollution in water and soil has been a major concern in the world's environmental issues. Nitrogen-containing compounds in the environment can cause new problems, such as river eutrofication and a dangerous disease called methemoglobinemia and other disorders in human health. One of the most important wastewater treatment goals is the removal of nitrogen, which is carried out by chemical, physical and biological processes that biological methods of nitrogen removal are more efficient and economical. Nitrification is one of the main processes for the removal of nitrate in water, a process that requires no oxygen, in which bacteria use from nitrate as an electron receiver to obtain energy for growth. Solid-phase denitrification process is an emerging technology which has received increasing attention in recent years. It uses biodegradable polymers as both the carbon source and biofilm carrier for denitrifying microorganisms this process is a promising technology for the removal of nitrate from water and wastewater. In the future, more attention can be devoted to the simultaneous removal of nitrate and other pollutants from water by Solid-phase denitrification, thereby ensuring the health of the environment and human.
https://ije.ut.ac.ir/article_67555_0fb63fe0385846481669cb2c3878e408.pdf
2018-09-23
875
889
10.22059/ije.2018.249987.805
nitrate pollution
Treatment of wastewater
solid-phase denitrification
denitrifiers
Mehdi
Zarabi
mzarabi@ut.ac.ir
1
University of Tehran
LEAD_AUTHOR
BAHAREH
KARIMI DOUNA
baharehkarimidouna72@gmail.com
2
UNIVERSITY OF TEHRAN
AUTHOR
Ashraf sadat
Hatamian zaremi
hatamian_a@ut.ac.ir
3
University of Tehran
AUTHOR
[1]. Choi J, Maruthamuthu. S, Lee H, Hyun Ha T, Bae J. Nitrate removal by electro-bioremediation technology in Korean soil. Journal of Hazardous Materials. 2009;168: 1208–1216
1
[2]. Bahadoran Z, Mirmiran P, Ghasemi A, Kabir A, Azizi F, Hadaegh F. Is dietary nitrate/nitrite exposure a risk factor for development of thyroid abnormality? A systematic review and meta-analysis. Nitric Oxide. 2015; 47: 65–76
2
[3]. Moorman, T.B, Parkin, T.B, Kaspar, T.C, Jaynes, D.B. Denitrification activity, wood loss, and N2O emissions over 9 years from a wood chip bioreactor. Ecol. Eng. 2010; 36: 1567–1574
3
[4]. Elgood, Z, Robertson, W.D, Schiff, S.L, Elgood, R. Nitrate removal and greenhouse gas production in a stream-bed denitrifying bioreactor. Ecol. Eng. 2010; 36: 1575–1580
4
[5]. Wang J, Hou W, Qian Y. Immobilization of microbial cells using polyvinyl alcohol (PVA)—polyacrylamide gels. Biotechnol Tech.1995; 9: 203–208
5
[6]. Manohar S, Karegoudar TB. Degradation of naphthalene by cells of Pseudomonas sp. strain NGK 1 immobilized in alginate, agar and polyacrylamide. Appl Microbiol Biotechnol.1998:49:785– 792
6
[7]. Son HJ, Kim HG, Kim KK. Increased production of bacterial cellulose by Acetobacter sp. V6 in synthetic media under shaking culture conditions. Bioresour Technol. 2003; 86:215–219.
7
[8]. Ba¨ckdahl H, Helenius G, Bodin A, Nannmark U. Mechanical properties of bacterial cellulose and interactions with smooth muscle cells. Biomaterials. 2006; 27:2141–2149
8
[9]. Svensson A, Nicklasson E, Harrah T, Panilaitis B, Kaplan DL. Bacterial cellulose as a potential scaffold for tissue engineering of cartilage. Biomaterials. 2005; 26:419–431
9
[10]. Wang J.L, Yang N. Partial nitrification under limited dissolved oxygen conditions. Process Biochem. 2004;39 (10): 1223–1229.
10
[11]. Chen, Z.Q, Wen, Q.X, Wang, J.L, Li, F. Simultaneous removal of carbon and nitrogen from municipal-type synthetic wastewater using net-like rotating biological contactor (NRBC). Process Biochem.2006; 41 (12): 2468–2472.
11
[12]. Liu Q.J, Hu X, Wang J.L. Performance characteristics of nitrogen removal in SBR by aerobic granules. Chin. J. Chem. Eng. 2005;13 (5): 669–672.
12
[13]. Aslan S, Turkman A. Biological denitrification of drinking water using various natural organic solid substrates. Water Sci. Technol.2003;48 (11−12): 489–495.
13
[14]. Wang J.L, Kang J. The characteristics of anaerobic ammonium oxidation (ANAMMOX) by granular sludge from an EGSB reactor. Process Biochem.2005;40 (5): 1973–1978.
14
[15]. Karanasios K.A, Vasiliadou, I.A, Pavlou S, Vayenas D.V, 2010. Hydrogenotrophic denitrification of potable water: a review. J. Hazard Mat. 2008;180 (1–3): 20–37.
15
[16]. Van Rijn J, Tal Y, Schreier H.J. Denitrification in recirculating systems: theory and applications. Aquac. Eng.2006; 34 (3):364–376.
16
[17]. Ovez B, Mergaert J, Saglam M. Biological denitrification in drinking water treatment using the seaweed Gracilaria verrucosa as carbon source and biofilm carrier. Water Environ. Res.2006;78 (4): 430–434.
17
[18]. Schipper L.A, Robertson W.D, Gold A.J, Jaynes D.B, Cameron S.C. Denitrifying bioreactors-an approach for reducing nitrate loads to receiving waters. Ecol. Eng.2010; 36 (11): 1532–1543
18
[19]. Bill K.A, Bott C.B, Murthy S.N. Evaluation of alternative electron donors for denitrifying moving bed biofilm reactors (MBBRs). Water Sci. Technol.2009; 60 (10): 2647–2657.
19
[20]. Modin O, Fukushi K, Yamamoto K. Denitrification with methane as external carbon source. Water Res. 2007;41 (12): 2726–2738.
20
[21]. Fan Z.X, Hu J, Wang J.L. Biological nitrate removal using wheat straw and PLA as substrate. Environ. Technol. 2012;33: 2369–2374.
21
[22]. Wang J, Chu L. Biological nitrate removal from water and wastewater by solid-phase denitrification process. Biotechnology Advances.2016.
22
[23]. Chu L.B, Wang J.L. Denitrification of groundwater using PHBV blends in packed bed reactors and the microbial diversity. Chemosphere.2016;155 (3): 463–470.
23
[24]. Boley A, Muller W.R. Denitrification with polycaprolactone as solid substrate in a laboratory-scale recirculated aquaculture system. Water Sci. Technol.2005;52 (10−11): 495–502.
24
[25]. Hiraishi A, Khan S.T. Application of polyhydroxyalkanoates for denitrification in water and wastewater treatment. Appl. Microb. Biotechnol. 2003;61 (2): 103–109.
25
[26]. Yadegari F, Abdollahzadeh sharghi E, Adl M. Biological denitrification of drinking water in an anoxic-oxic membrane bioreactorr by suspended activated sludge. fourth national chemistry, petrochemicstry and nano conference, Iran, Tehran, Petrograd Industrial and Mineral Research Center- petrogas.2016 [Persian].
26
[27]. Tangsir1 S, Naseri A.A, Moazed H, Hashemi Garmdareh S.E, Broumand Nasab S- Evaluate the Performance of Sugarcane Bagasse as a Carbonic Source Required in the Design of Denitrification Substrates. Irrigation Sciences and Engineering. 2017; 40(2)39-57 [Persian].
27
[28]. Godini H, Rezayi A, Biranvand F, Jahanbani F. Removal of nitrate from water by using a Consortium of denitrifiers stabilized on active carbon in a floating bed reactor, 2012. Lorestan [Persian].
28
[29]. Vagheei R, Ganjidoust H, Azimi A. A, Ayati B.Treatment of Nitrate-contaminated Drinking Water Using Autotrophic Denitrification in a Hydrogenised Biofilter, 2009[Persian].
29
[30]. Mir bagheri S.A, Amir soleymani A, Bazaz zadeh R. Treatment of nitrate-contaminated ground water by denitrification using heterotrophic and autotrophic bacteria (Case Study for Groundwater Refinery of Tehran). Second International Symposium on Environmental Engineering, Tehran, Industrial University of Khajeh Nasir.2009 [Persian].
30
[31]. Lijuan Feng a, Kun Chen a, Doudou Han a, Jing Zhao a, Yi Lua, Guangfeng Yang a, Jun Mua, Xiangjiong Zhao. Comparison of nitrogen removal and microbial properties in solid-phase denitrification systems for water purification with various pretreated lignocellulosic carriers. Bioresource Technology 224 (2017) 236 245
31
[32]. Shen, Z.Q, Wang J.L. Biological denitrification using cross-linked starch/PCL blends as solid carbon source and biofilm carrier. Bioresour. Technolo.2011; 102 (19): 8835–8838.
32
[33]. Khan SH, Horiba Y, Takahashi N, Hirashi A. Activity of community composition of denitrifying bacteria in Poly (3- hydroxybutyrate-co-3-hydroxyvalerate)- using Solid phase denitrification processe. Microbs environ.2007; 22 (1): 20-31.
33
[34]. Gibert O, Pomierny S, Rowe I, Kalin R.M. Selection of organic substrates as potential reactive materials for use in a denitrification permeable reactive barrier (PRB). Bioresour. Technol. 2008;99 (16): 7587–7596.
34
[35]. Healy M.G, Ibrahim T.G, Lanigan G.J, Serrenho A.J, Fenton O. Nitrate removal rate, efficiency and pollution swapping potential of different organic carbon media in laboratory denitrification bioreactors. Ecol. Eng. 2012; 40: 198–209.
35
[36]. Volokita M, Belkin S, Abeliovich A, Soares M.I.M. Biological denitrification of drinking water using newspaper. Water Res.1996; 30 (4): 965–971.
36
[37]. Aslan S, Turkman A. Simultaneous biological removal of endosulfan (alpha plus beta) and nitrates from drinking waters using wheat straw as substrate. Environ. Int.2004; 30 (4): 449–455.
37
[38]. Xu Z.X, Shao L, Yin H.L, Chu H.Q, Yao Y.J. Biological denitrification using corncobs as a carbon source and biofilm carrier. Water Environ. Res.2009; 81 (3): 242–247.
38
[39]. Robertson W.D. Nitrate removal rates inwoodchipmedia of varying age. Ecol. Eng.2010; 36 (11): 1581–1587.
39
[40]. Cameron S.G, Schipper L.A. Hydraulic properties, hydraulic efficiency and nitrate removal of organic carbon media for use in denitrification beds. Ecol. Eng. 2012;41: 1–7.
40
[41]. Walters E, Hille A, He M, Ochmann C, Horn H. Simultaneous nitrification denitrification in a biofilm airlift suspension (BAS) reactor with biodegradable carriermaterial. Water Res.2009; 43 (18): 4461–4468.
41
[42]. Zhao X, Meng X.L, Wang J.L. Biological denitrification of drinking water using biodegradable polymer. Int. J. Environ. Pollut.2009; 38 (3): 328–338.
42
[43]. Zhou H.H, Zhao X, Wang J.L. Nitrate removal fromgroundwater using biodegradable polymers as carbon source and biofilm support. Int. J. Environ. Pollut.2009; 38 (3): 339–348.
43
[44]. Shen Z.Q, Wang J.L. Biological denitrification using cross-linked starch/PCL blends as solid carbon source and biofilm carrier. Bioresour. Technolo.2011;102 (19): 8835–8838.
44
[45]. Takahashi M, Yamada T, Tanno M, Tsuji H, Hiraishi A. Nitrate removal efficiency and bacterial community dynamics in denitrification processes using poly (L-lactic acid) as the solid substrate. Microb. Environ. 2011; 26 (3):212–219.
45
[46]. Wu W.Z, Yang L.H, Wang J.L. Denitrification using PBS as carbon source and biofilm support in a packed-bed bioreactor. Environ. Sci. Pollut. Res. 2013; 20 (1):333–339.
46
[47]. Warneke S, Schipper L.A, Matiasek M.G, Scow K.M, Cameron S, Bruesewitz D.A, McDonald I.R. Nitrate removal, communities of denitrifiers and adverse effects in different carbon substrates for use in denitrification beds. Water Res. 2011;45:5463–5475.
47
[48]. Gutierrez-Wing M.T, Malone R.F, Rusch K.A. Evaluation of polyhydroxybutyrate as a carbon source for recirculating aquaculture water denitrification. Aquac. Eng.2012; 51:36–43.
48
[49]. Tokiwa Y, Calabia B, Ugwu C, Aiba S. Biodegradability of plastics. Int. J. Mol. Sci.2009;10 (9): 3722
49
[50]. Chen, Q.H., Li, X.F., Lin, J.H. Preparation and properties of biodegradable bamboo powder/polycaprolactone composites. J. Forest. Res. 2009; 20 (3): 271–274.
50
[51]. Zhang J, Feng C, Hong S, Hao H, Yang Y.Behavior of solid carbon sources for biological denitrification in groundwater remediation. Water Sci. Technol.2012.
51
[52]. Schipper L.A, Robertson W.D, Gold A.J, Jaynes D.B, Cameron, S.C. Denitrifying bioreactors-an approach for reducing nitrate loads to receiving waters. Ecol. Eng. 2010; 36 (11): 1532–1543.
52
[53]. Muller W.R, Heinemann S, Wurmthaler R.T. Aspects of PHA (poly- B-hydroxy-butyric- acid) as an h-donator for denitrification in water treatment processes. Water Supply.1992;10: 79–90.
53
[54]. Zhang Q, Ji F, Xu X. Effects of physicochemical properties of poly-ε-caprolactone on nitrate removal efficiency during solid-phase denitrification. Chem. Eng. J.2016; 283: 604–613.
54
[55]. Canziani R, Vismara R, Basilico D, Zinni L. Nitrogen removal in fixed-bed submerged biofilters without backwashing. Water Sci. Technol.1999;40 (4–5): 145–152.
55
[56]. Wang X.M, Wang J.L.Nitrate removal from groundwater using solid-phase denitrification process without inoculating with external microorganisms. Int. J. Environ. Sci. Technol. 2013;10:955–960.
56
[57]. Lin Y.F, Jing S.R, Lee D.Y, Chang Y.F, Shih K.C. Nitrate removal from groundwater using constructed wetlands under various hydraulic loading rates. Bioresource Technology, 2008;99: 7504-7513.
57
[58]. Ghane E, Fausey N.R, Brown L.C. Modeling nitrate removal in a denitrification bed. Water Research, 2015; 71: 294-305.
58
[59]. Ines M, Soares M, Abeliovich A. Wheat straw as substrate for water denitrification. Water Res.1998; 32 (12): 3790–3794.
59
[60]. Boley A, Muller W.R, Haider G. Biodegradable polymers as solid substrate and biofilm carrier for denitrification in recirculated aquaculture systems. Aquac. Eng. 2000;22 (1–2): 75–85.
60
[61]. Zhu S.M, Deng Y.L, Ruan Y.J, Guo X.S, Shi M.M, Shen J.Z. Biological denitrification using poly (butylene succinate) as carbon source and biofilm carrier for recirculating aquaculture system effluent treatment. Bioresour. Technol. 2015;192: 603–610.
61
[62]. Li P, Zuo J, Wang Y, Zhao J, Tang L, Li Z. Tertiary nitrogen removal for municipal wastewater using a solid-phase denitrifying biofilter with polycaprolactone as the carbon source and filtration medium. Water Res. 2016; 93: 74–83.
62
[63]. Chu L.B, Wang J.L. Denitrification performance and biofilm characteristics using biodegradable polymers PCL as carriers and carbon source. Chemosphere. 2013; 91 (9):1310–1316.
63
[64]. Blaszczyk M. Effect of various sources of organic carbon and high nitrite and nitrate concentrations on the selection of denitrifying bacteria. II. Continuous cultures in packed bed reactors. Acta Microb. Pol. 1982; 32 (1): 65–71.
64
[65]. HondaY, Osawa Z. Microbial denitrification of wastewater using biodegradable polycaprolactone. Polym. Degrad. Stab.2002; 76 (2): 321–327.
65
[66]. Lee N.M, Welander T. The effect of different carbon sources on respiratory denitrification in biological wastewater treatment. J. Ferment. Bioeng.1996; 82 (3):277–285.
66
[67]. Neef A, Zaglauer A, Meier H, Amann R, Lemmer H, Schleifer K.H. Population analysis in a denitrifying sand filter: conventional and in situ identification of Paracoccus spp. in methanol-fed biofilms. Appl. Environ. Microbiol.1996; 62 (12): 4329–4339.
67
[68]. Shen Z.Q, Zhou, Y.X, Wang J.L. Comparison of denitrification performance and microbial diversity using starch/polylactic acid blends and ethanol as electron donor for nitrate removal. Bioresour. Technol.2013;131; 33–39.
68
[69]. Wu W.Z, Yang L.H, Wang J.L. Denitrification performance and microbial diversity in a packed-bed bioreactor using PCL as carbon source and biofilm carrier. Appl. Microb. Biotechnol.2013; 97 (6), 2725–2733.
69
[70]. Cang Y, Roberts DJ, Clifford DA. Development of cultures capable of reducing perchlorate and nitrate in high salt solutions. Water Res. 2004; 38:3322- 3330.
70
[71]. Aslan U, Turkman A. Combined biological removal of nitrate and pesticides using wheat straw as substrate. Process Biochem. 2005;40: 935-943.
71
[72]. Zhu S, Zheng M, Li C, Gui M, Chen Q, J Ni.Special role of corn flour as an ideal carbon source for aerobic denitrification with minimized nitrous oxide emission. Bioresource Technology, 2015;186: 44-51.
72
ORIGINAL_ARTICLE
Developing Fuzzy Optimization Model for Conjunctive Use of Surface and Ground Water, Case Study: Astaneh-Koch Esfahan Plain
In this study, an entirely fuzzy optimization model is presented for conjunction use of surface and groundwater. Groundwater level in Astaneh-Koch Esfahan Aquifer was simulated using the GMS Model, while its results were used as a constraint in optimization model. Then, Kumar and Jayalakishimi fuzzy optimization methods were solved by applying the GAMS software. Maximum water supply shortage in Kumar method for left-side of Sangar was in 2009 that 58.36% of demands was satisfied. Also this value in the right-side was calculated about 56.76% in 2008. In the Jayalakishimi method, the maximum water supply shortage was obtained in 1998 and 2014 that 66.5% and 60.96% of demands for left and right-side are satisfied, respectively. On the other hand, for this method, in the situation of total maximum shortage for left and right sides of Sangar channel, supply percentage of water needs was about 65.9% in 1998, while for the Jayalakishimi method, it was obtained about 66.5% in 1998. Also in the current situation, the supply percentage in the worst conditions is 54%. Regarding consideration of uncertainties, the proposed fuzzy optimization model can be applied to manage the conjunctive water supply for agriculture.
https://ije.ut.ac.ir/article_67557_7abcbb86baa089af47c44b0c3b05ffd0.pdf
2018-09-23
891
905
10.22059/ije.2018.250098.807
Kumar Method
Jayalakishimi Method
Groundwater Simulation
Sefidroud
MODFLOW
Sami
Ghordoyee Milan
s.milan@ut.ac.ir
1
MSc of Water Resources Engineering, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran.
AUTHOR
Abbas
Roozbahani
roozbahany@ut.ac.ir
2
Assistant Professor, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran
LEAD_AUTHOR
Mohammad Ebrahim
Banihabib
banihabib@ut.ac.ir
3
Associate Professor, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran
AUTHOR
Saman
Javadi
javadis@ut.ac.ir
4
Assistant Professor, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran
AUTHOR
[1]. Todd KD, Mays LW. Groundwater hydrology. John Wiley & Sons, Inc, NJ. 2005.
1
[2]. Buras N. Conjunctive operation of dams and aquifers. Journal of the Hydraulics Division. 1963; 89(6):111-31.
2
[3]. Abadi A, Kholghi A, Bozorg Hadad O, Mohammadi K. Providing operational rules for real-time management of surface water and underground water resources. MSc Thesis.2010. University of Tehran, Pardis-Karaj. [Persian]
3
[4]. Bazargan-Lari, MR, Kerachian R, Mansoori A. A Conflict-Resolution Model for the Conjunctive Use of Surface and Groundwater Resources that Considers Water-Quality Issues: A Case Study. Environmental management. 2009; 43(3):470-82.
4
[5]. Karamouz M, Tabari. MMR, Kerachian R. Application of genetic algorithms and artificial neural networks in conjunctive use of surface and groundwater resources. Water International. 2007; 32(1):163–176.
5
[6]. Mohammad Rezapour Tabari M, Ebadi T, Maknon R. Development of a Smart Model for Groundwater Level Prediction Based on Aquifer Dynamic Conditions. Water and Wastewater Journal. 2010; Volume: 21(4): 70-80. [Persian]
6
[7]. Azari, A, Radmanesh F. Simulation-Multi-Purpose Optimization for Integrated Water Resources Management in Surface Water and Underground Water Interactions Using Genetic Algorithm (Case Study: Dasht Daz), MSc Thesis.2013; Shahid Chamran University of Ahvaz. [Persian]
7
[8]. Safavi H.R, Alijanian M.A. Optimal Crop Planning and Conjunctive Use of Surface Water and Groundwater Resources Using Fuzzy Dynamic Programming. Journal of irrigation and drainage engineering © ASCE. 2011; 383-397.
8
[9]. Safavi HR, Chakraei I, Kabiri-Samani A, Golmohammadi MH. Optimal reservoir operation based on conjunctive use of surface water and groundwater using neuro-fuzzy systems. Water resources management. 2013; 27(12):4259-75.
9
[10]. Safavi HR, Enteshari S. Conjunctive use of surface and ground water resources using the ant system optimization. Agricultural Water Management. 2016; 173:23-34.
10
[11]. Tabari MMR. Conjunctive Use Management under Uncertainty Conditions in Aquifer Parameters. Water Resources Management. 2015; 29(8):2967-86.
11
[12]. Chang L-C, Chu H-J, Chen Y-W. A fuzzy inference system for the conjunctive use of surface and subsurface water. Advances in Fuzzy Systems. 2013; 2013:2.
12
[13]. Rezaei F, Safavi HR, Mirchi A, Madani K. F-MOPSO: an alternative multi-objective PSO algorithm for conjunctive water use management. Journal of Hydro-environment Research. 2017; 14:1-18.
13
[14]. Safavi H, Rezaei F. Conjunctive use of surface and ground water using fuzzy neural network and genetic algorithm. Iranian Journal of Science and Technology Transactions of Civil Engineering. 2015; 39(C2):365.
14
[15]. Rezaei F, Safavi HR, Zekri M. A hybrid fuzzy-based multi-objective PSO algorithm for conjunctive water use and optimal multi-crop pattern planning. Water Resources Management. 2017; 31(4):1139-55.
15
[16]. Langeroudi M.R, Kerachian R. .Developing Operating Rules for Conjunctive Use of Surface and Groundwater Considering the Water Quality Issues. KSCE Journal of Civil Engineering. 2014; 18(2):454-461.
16
[17]. Chen, Y. W., Chang, L. C., Huang, C. W. and Chu, H. J. Applying genetic algorithm and neural network to the conjunctive use of surface and subsurface water. Water resources management.2013; 27: 4731-4757.
17
[18]. Sahoo, B., Lohani, A. K. and Sahu, R. K. Fuzzy multiobjective and linear programming based management models for optimal land-water-crop system planning. Water resources management. 2006;20: 931-948.
18
[19]. Li, M., Fu, Q., Singh, V. P., Ma, M. and Liu, X. An intuitionistic fuzzy multi-objective non-linear programming model for sustainable irrigation water allocation under the combination of dry and wet conditions. Journalof Hydrology. 2017;
19
[20]. Asaadi Mehrabani, M. Banihabib, M. Roozbahany A. Fuzzy Linear Programming Model for the Optimization of Cropping Pattern in Zarrinehroud Basin. Iran Water Resources Research. 2018; 14(1), 13-24. [Persian]
20
[21]. Continuation of the study of the plains with a quantitative and qualitative quantitative and qualitative study network of Astaneh-Kochi Esfahan 1389-1390, [Persian]
21
[22]. Pandam Consulting Engineers. Improvement of irrigation and drainage network in Gilan aquifer. 1383; Volume Four. [Persian]
22
[23]. Harbaugh AW, Banta ER, Hill MC, McDonald MG. MODFLOW-2000, the U.S. Geological Survey modular groundwater model.2000; Report No. 00–92, U.S. Geological Survey, Denver.
23
[24]. Klir GJ, Yuan B. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A. Zadeh: World Scientific Publishing Co., Inc.; 1996.
24
[25]. Zadeh, L.A. Fuzzy sets. J. of Information and Control, 1965; 8(3), 338-353.
25
[26]. Kumar A, Kaur J, Singh P. Fuzzy optimal solution of fully fuzzy linear programming problems with inequality constraints. 2010.
26
[27]. Kumar A, Kaur J, Singh P. A new method for solving fully fuzzy linear programming problems. Applied Mathematical Modelling. 2011; 35(2):817-23.
27
[28]. Jayalakshmi M, Pandian P. A new method for finding an optimal fuzzy solution for fully fuzzy linear programming problems. International Journal of Engineering Research and Applications. 2012; 2(4):247-54.
28
ORIGINAL_ARTICLE
Flow modeling in a bend of a natural river based on different turbulence models(case study:Doab Samsami River)
Flow modeling in rivers is very complicated due to their meandering path. Therefore, the use of an accurate numerical model for predicting flow pattern and the effects of flow turbulence is necessary. Furthermore, choosing the type of turbulence model can be effective in simulating and studying flow properties. Different types of turbulence models can be used in SSIIM numerical model. In this study, with the aim of investigating the efficiency of turbulence models, three different kindes of k-Ԑ turbulence model,including the standard type, based on water velocity and RNG, are used to simulate flow characteristics in different points of a 45 degrees cross-section from a steep bend located on the Doab Samsami river. Comparing the measured values of the velocity component with the results of the model indicated that the standard k-Ԑ model for determining the vertical component of velocity and k-Ԑ model based on water velocity for the longitudinal component of the velocity are best models in accuracy, respectively and generally the ability of all mentioned the turbulence models evaluated well. Moreover, exact consideration of bed roughness and roughness of the channel bank in the numerical model can have a significant effect on the accuracy of the model results.
https://ije.ut.ac.ir/article_67558_e8b117ff013a148a3a6b3c9e598ed6eb.pdf
2018-09-23
907
916
10.22059/ije.2018.252171.827
k-Ԑ turbulence model
SSIIM numerical model
Doab Samsami River
RNG
afshin
honarbakhsh
afshin.honarbakhsh@gmail.com
1
Associate Professor of range and Watershed Management Faculty of Natural Resources and Earth Sciences, Shahrekord University
LEAD_AUTHOR
Rouhollah
Karimian kakolaki
karimian.roh@gmail.com
2
Ph.D. student of Watershed Science and Engineering Faculty of Natural Resources and Earth Sciences, Shahrekord University, Iran
AUTHOR
gholamreza
shams ghahfarokhi
g.shams@eng.sku.ac.ir
3
Assistant Professor, Department of Civil Engineering, Faculty of Engineering, Shahrekord University.
AUTHOR
alireza
davoudian dehkordi
alireza.davoudian@gmail.com
4
Full Professor, Department of Petrology, Faculty of Natural Resources and Earth Sciences, Shahrekord University.
AUTHOR
mehdi
pajouhesh
drpajoohesh@gmail.com
5
head of rangeland and watershed management group
AUTHOR
[1]. Yousefi, H, Golshan,M , Pirnia, A. Evaluation of HEC-HMS Hydrological Model in estimating Flood Hydrograph of Dry and Humid regions. Journal of Ecohydrology, 2018; 5(1): 319 – 330. .)Persian(.
1
[2]. Yousefi, H, Ehara,S ,Noorollahi, Y. Modifying the analysis made by water quality index using multi-criteria decision making methods. Journal of African Earth sciences, 2018; 138, 309 – 318.
2
[3]. Yousefi, H, Tavakkoli-Moghaddam, R, Oliaei, MTB, Mohammadi, M. Solving a bi-objective vehicle routing problem under uncertainty by a revised multichoice goal programming approach. International Journal of Industrial Engineering Computations, 2017; 8(3):283-302
3
[4]. Xiufang Z. Pingyi W. and Y. Chengyu. Experimental study on flow turbulence distribution around a spur dike with different structure. Journal of Procedia Engineering,2015; 28: 772-775.
4
[5]. Hao, Z. Hajime, N. kenji, K. and B. Yasyuki. Experiment and simulation of turbulent flow in local scour around a spur dyke. Journal of Sediment Research, 2014; 24: 33-45.
5
[6]. Lai, Y. G. and Greimann, B.P. Predicting contraction scour with two-dimensional depth averaged model. Journal of Hydraulic Research, 2010; 48(3): 383-387.
6
[7]. Khosravi, G. The numerical simulation of flow and sediment transport with model CCHE2D (Case Study: meander downstream Minab). Master's thesis, University of Hormozgan, Bandarabbas, Iran. 2012. (Persian).
7
[8]. Fathi, M., A. Honarbakhsh, M. Rostami and D. Davoodian Dehkordi. simulating the flow pattern with a two-dimensional numerical model in a range of natural meanders, Case Study: Khoshkerood Farsan River, Chaharmahal and Bakhtiari, Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Sciences. 2012; 62(1): 95-108. (Persian).
8
[9]. Andersson. B, Andersson.R, Hakansson. L, Mortensen. M, Sudiyo. R and vanWachem. B. Computational fluid dynamics for engineers. Cambridge University Press, 2012.
9
[10]. Rodi, W. and Leschziner M. A. Calculation of Strongly Curved Open Channel Flow. Journal of the Hydraulic Division,1978; 105(HY10) : 1297-1333.
10
[11]. Shettar, A.S., and Murthy, K.K.A numerical study of division of flow in open channels, Journal of Hydraulic Research, Delft, The Netherlands,1996; 34)5( :651-675.
11
[12]. Han, S.S. Characteristics of flow around 90 open channel bends. PhD. Thesis. Dept. of Building,Civil and Environmental Engineering, Concordia University, Montreal, Quebec.2010.
12
[13]. Van Balen, W., Uijttewaal, W.S.J., and Blanckaert, K. Large eddy simulation of a mildly curved open channel flow", J. Fluid Mech.2009; 630(1): 413-442.
13
[14]. SafarzadehGandshamin, A., and Salehi Neishabouri, A. A. Numerical study of turbulent flow pattern and qualitative study of sediment transport and erosion in lateral drainage from the river. Journal of Modarres Technical and Engineering,2007; 25(1) : 1-18.( Persian).
14
[15]. Omid Beygi, M. A. Laboratory study and numerical simulation of three dimensional flow pattern in lateral drainage of the river in the presence of submerged panels. Msc Thesis. Dept. of Agriculture, Tarbiat Modarres University.2010.( Persian).
15
[16]. Mozaffari, J., Samadi, A., Mohseni Movahhed, S. A., Davoud-Maghami, D. Comparison of RSM and LES Turbulence Models on Sharp Bend. Journal of Ferdowsi Civil Engineering, 2015; 27(1): 77-86. (Persian).
16
[17]. Zhang, M. L., and Shen, Y. M. Three-dimensional simulation of meandering river based on 3-D RNG k-ɛ turbulence model. Journal of Hydrodynamics,2008; 20(4) : 448-455.
17
[18]. Yu, L. R. Flow and transport simulation of Madeira River using three depth-averaged two-equation turbulence closure models. Water Science and Engineering, 2012. 5(1): 11-25
18
[19]. Wu, W. CCHE2D Sediment Transport Model (Version 2.1). Tech Report No. NCCHETR- 2001-3, NCCHE, University of Mississippi, 2009. USA, P: 12.
19
[20]. Cea, L., Pena, L., J. Puertas, J., M. E. Vazquez-Cendon, M. E., and Pena, E. Application of several depth-averaged turbulence models to simulate flow in vertical slot fishways. Journal of Hydraulic Engineering, 2007;133(2):160–172.
20
[21]. Launder, B. E., and Spalding, D. B. The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering, 1974; 3(2): 269-289.
21
ORIGINAL_ARTICLE
Mechanism of N- Nitrate pollution of Kashan plain aquifer
In this research, the hydrogeochemical situation of Kashan aquifer, its pollution to nitrate and nitrite and their mechanism were studied. For this purpose, 42 water samples from the aquifer were prepared and analyzed. The results showed that chloride and sulfate are predominant anions and sodium and calcium are dominant cations. The results about the permitted range of TDS, pH, Na+, K+, Cl-, SO42-, NO3-, NO2-, Ca2+ and Mg2+ variables showed that respectively, 97.61%, 40.47%, 100%, 97.61%, 95.23%, 38.09%, 95.24%, 83.34% and 95.23% of region water resources have unauthorized status according to WHO and ISIRI standards. Correlation between NO3- with Na+, K+, Ca2 +, Mg2+, Cl-, SO42-, NO2-, TDS and EC was 0.68, 0.50, 0.63, 0.52, 0.64, -0.34, 0.32, 0.64, 0.64, respectively. other results showed that nitrate varied between 1.86 to 1034 and had an average of 118.76 mg/lit. Also, 40.49%, 21.42% and 38.9% of the samples for nitrate were slightly polluted, polluted and highly polluted, respectively. For more precise investigation of aquifer contamination, based on WHO recommendation, the combined results of nitrate and nitrite showed that 95.23% of the samples had a non-allowable concentration. Therefore, only small parts of the aquifer in the south, southwest and west have acceptable situation.
https://ije.ut.ac.ir/article_67560_0d44c55fd2a0d81a341f26e5571120da.pdf
2018-09-23
917
929
10.22059/ije.2018.252177.828
Kashan Aquifer
Salinization
Contamination mechanism
Nitrate. Nitrite
Mohammad
Mirzavand
mmirzavand23@yahoo.com
1
Department of Range and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran
AUTHOR
Hoda
Ghasemieh
h.ghasemieh@kashanu.ac.ir
2
Department of Range and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran
LEAD_AUTHOR
Seid Javad
Sadatineghad
jsadatinejad@ut.ac.ir
3
Department of Renewable Energies and Environment, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
AUTHOR
Bagheri
Nik Ghojogh
r.bagheri@shahroodut.ac.ir
4
Faculty of Earth Sciences, Shahrood University of Technology, Shahrood, Iran
AUTHOR
Ian Douglas
Clark
idclark@uottawa.ca
5
Department of Earth and Environmental Sciences, University of Ottawa, Canada
AUTHOR
]1]. Singh ET, Gupta A, Singh NR. Groundwater quality in Imphal West district, Manipur, India, with multivariate statistical analysis of data. Environmental Science and Pollution Research International. 2013; 20:2421-30.
1
]2]. Karami GH, Jafari H, Ghanaatian H. Contamination of groundwater resources in the agricultural land of Magan plain, Semnan province. Journal of Advanced Applied Geology. 2016; 21:45-55 [Persian].
2
[3]. Pawar N., Sheikh I. Nitrate pollution of ground waters from shallow basaltic aquifers, Deccan Trap Hydrologic Province, India. Environmental Geology. 1995;25(3):197–204.
3
[4]. Gordon B, Callan P, Vickers C. WHO guidelines for drinking-water quality. World Health Organization (WHO). 4th ed. Geneva. Switzerland. 2008;38(3): 564 p.
4
[5]. ISIRI. Drinking water physical and chemical specification. Institute of Standard and Industrial Research of Iran, Tehran. 1053. 5th revision. 2013;26p [Persian].
5
[6]. Amarlooei A, Nazeri M, Sayeh miri K, Nourmoradi H, Sayehmiri K, Khodarahmi F. Investigation on the concentration of nitrate and nitrite in Ilam ground waters. Scientific Journal of Ilam University of Medical Sciences (sjimu). 2014;22(4):34-41 [Persian].
6
[7]. Khodai K, Mohammadzadeh H, Nasseri H, Shahsavari AH. Evaluating of nitrate contamination in Dezful-Andimeshk plain and identifying of nitrate sources using 15N and 18O Isotopes. Iranian Journal of Geology. 2012; 22(6):93-111 [Persian].
7
]8]. Majumdar D, Gupta N. Nitrate pollution of ground water and associated human health disorders. Indian Journal of Environmental Health. 2000;42(1):28–39.
8
[9]. Jafari Malekabadi A, Afyuni M, Mousavi S.F, Khosravi A. Nitrate concentration In groundwater In Isfahan province. Journal of Water and Soil Science (JWSS). 2004;8(3):69-82 [Persian].
9
]10]. Khosravi Dehkordi A, Afyuni M, Mousavi S.F. Investigation of nitrate concentration changes of groundwater in Zayandehroud margin in Isfahan province. Journal of Environmetal Studies. 2006;32(39):33-40 [Persian].
10
[11]. Selek Z, Yetis A. Assessment of nitrate contamination in a transnational groundwater basin: a case study in the Ceylanpinar Plain, Turkey. Environ Earth Science. 2017;76:698: 1-11.
11
[12]. Mirzavand M, Ghazavi R, Sadatinejad S., Ghasemieh H, Vali A. Investigation of Kashan aquifer situation using electric resistance method with Shelomberje arrangement. Desert Ecosystem Engineering Journal. 2014;3(4):43-56 [Persian].
12
[13]. Mirzavand M, Ghazavi R. A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water Resources Management. 2015;29(4):1315–1328.
13
]14]. Clark ID. Groundwater geochemistry and isotopes. Taylor & Francis Group; 2015. 471 p.
14
]15]. http://www.aqion.de/site/92.
15
[16]. Amiri V, Nakhaei M, Lak R, Kholghi M. Assessment of seasonal groundwater quality and potential saltwater intrusion: a study case in Urmia coastal aquifer (NW Iran) using the groundwater quality index (GQI) and hydrochemical facies evolution diagram (HFE-D). Stoch Environ Res Risk Assess. 2016;30(5):1473–1484.
16
]17]. Mehdinia SM, Nikravesh SH. Determining contamination of drinking water distribution network in Damghan city. Journal of Water & Wastewater. 2002;43:60-1 [Persian].
17
[18]. Menció A, Mas-pla J, Otero N, Regàs O, Boy-roura M, Puig R, et al. Nitrate pollution of groundwater ; all right … , but nothing else ? Science of Total Environment. 2015; 539:241-251.
18
]19]. Xing L, Guo H, Zhan Y. Groundwater hydrochemical characteristics and processes along flow paths in the North China plain. Journal of Assian Earth Science. 2013;70-71:250-64.
19
ORIGINAL_ARTICLE
Improvement of Estimation of Flood Hydrograph Using Modified Curve Number (non-linear Ia-S) Model
The Curve Number Model (SCS-CN) is in conventional mode is based on the linear relationship between initial absorption (Ia) and potential maximum retention (S) of the catchment but this model has been modified to consider non-linear Ia-S relation. The objective of this study is to compare the conventional curve number and modified curve number (non-linear Ia-S relation) models in flood hydrograph estimation in five Galikesh, Nodeh, Tamer, Vatana and Kechik catchments (37 rainfall-runoff events in calculation and selection of 14 events for results comparison in validation step). The root mean square error (RMSE), Nash-Sutcliff (NSE) and peak discharge estimation error (PEP) criteria were used for results comparison. Investigation of RMSE and NSE and PEP criteria shows that the application of modified curved number model (non-linear Ia-S) in all events of validation step improves the estimations of flood hydrograph and peak discharge in comparison with conventional curve number model (SCS-CN), therefore the results indicated that in studied catchments, the modified curve number model (non-linear Ia-S) has improved the conventional curve number SCS-CN model.
https://ije.ut.ac.ir/article_67659_90e44691ca2ffdf8be1e8b19e4bc127a.pdf
2018-09-23
931
939
10.22059/ije.2018.252799.832
Flood
Hydrograph
Modified Curve Number (non-linear Ia-S)
curve number (SCS-CN)
Sanaz
Daei
sanazdaei826@yahoo.com
1
MSc Graduated, Water Engineering Department, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources.
AUTHOR
Meysam
Salari Jazi
meysam.salarijazi@gmail.com
2
استادیار، گروه مهندسی آب، دانشکدۀ مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان
LEAD_AUTHOR
Khalil
Ghorbani
ghorbani.khalil@yahoo.com
3
Associate Professor, Water Engineering Department, College of Water & Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources
AUTHOR
Mahdi
Meftah Halaghi
meftah_20@yahoo.com
4
Associate Professor, Water Engineering Department, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources.
AUTHOR
[1].King K W and Balogh, J. C. Curve numbers for golf course watersheds. Transactions of the ASABE. 2008; 51(3):987-996.
1
[2].Tramblay Y, Bouvier C, Martin C, Didon-Lescot J. F, Todorovik D, Domergue J. M. Assessment of initial soil moisture conditions for event-based rainfall–runoff modelling. Journal of Hydrology. 2010;387(3):176-187.
2
[3].Chung W. H, Wang I. T, Wang R. Y. Theory-based SCS-CN method and its applications. Journal of Hydrologic Engineering. 2010; 15(12):1045-1058.
3
[4].Ponce V. M, Hawkins R. H. Runoff curve number: Has it reached maturity. Journal of hydrologic engineering. 1996; 1(1):11-19.
4
[5].Soulis K. X, Valiantzas J. D. SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds-the two-CN system approach. Hydrology and Earth System Sciences. 2012; 16(3): 1001-1015.
5
[6].Romero P, Castro G, Gomez J. A, Fereres E. Curve number values for olive orchards under different soil management. Soil Science Society of America Journal. 2007; 71(6):1758-1769.
6
[7].Hawkins R. H. Asymptotic determination of runoff curve numbers from data. Journal of Irrigation and Drainage Engineering. 1993; 119(2):334-345.
7
[8].Ebrahimian M, Nuruddin A.A, MohdSoom M.A.B, Sood, A.M. Application of NRCS-curve number method for runoff estimation in a mountainous watershed. Caspian J. Environ. Sci. 2012; 10: 103-114.
8
[9].Cazier D.J, Hawkins R.H.Regional application of the curve number method. Water today and tomorrow. Proc. ASCE Irrigation and Drainage Division Special Conf., ASCE. New York. 1984; 710.
9
[10].Woodward D.E, Hawkins R.H, Hjelmfelt J.R, Jr A.T, Mullem J.A, Quan, Q.D. Runoff curve number method: Examination of the initial abstraction ratio. Proc. world water Environ. Res. Congress.2003; 1-10.
10
[11].Shi Z.H, Chen L.D, Fang N.F, Qin, D.F, Cai C.F. Research on SCSCN initial abstraction ratio using rainfall runoff event analysis in the three Gorges Area, China. Catina. 2009;77(1): 1-7.
11
[12].Singh M. Simulating rainfall changes effects on runoff and soil erosion in submontane Punjab. M.Sc. Thesis. Punjab Agricultural University. Ludhiana. 2014.
12
[13].Bales J, Betson R. P. The curve number as a hydrologic index. Rainfall Runoff Relationship. 1981; 371-386.
13
[14].Hauser V. L, Jones O. R. Runoff curve numbers for the Southern High Plains. Transactions of the ASAE. 1991; 34(1):142-148.
14
[15].Gao Y, Zhu, B, Miao C. Y. Application of SCS model to estimate the volume of rainfall runoff in sloping field of purple soil. Chinese Agricultural Science Bulletin. 2006; 22(11): 396-400.
15
[16].Mishra S K, and Singh V. P. Another look at SCS-CN method. Journal of Hydrologic Engineering. 1999; 4(3): 257-264.
16
[17].Mishra S K, Jain M K, Singh V P. Evaluation of the SCS-CN-based model incorporating antecedent moisture. Water resources management. 2004; 18(6): 567-589.
17
[18].Mishra S. K, Sahu R K, Eldho T I, Jain M K. An improved Ia- S relation incorporating antecedent moisture in SCS-CN methodology. Water Resources Management. 2006; 20(5): 643-660.
18
[19].Jiao P Xu D, Wang S, Yu Y, Han S. Improved SCS-CN method based on storage and depletion of antecedent daily precipitation. Water ResourManag. 2015; 29:4753–4765.
19
[20].Sahu R. K, Mishra S. K, Eldho T. I. An improved AMC‐coupled runoff curve number model. Hydrological processes. 2010;24(20): 2834-2839.
20
[21].Karn A L, Lal M, Mishra S K, Chaube U C , Pandey A. Evaluation of SCS-CN Inspired models and their comparison. Journal of Indian Water Resources Society. 2016; 36(3): 19-27.
21
[22].Mishra S. K, Singh V. P. Soil conservation service curve number (SCS-CN) methodology. 42th ed. Springer Science and Business Media.. 2013.
22
[23].Mishra S.K, Singh V.P. SCS-CN-based hydrologic simulation pack-age. In: Singh V.P,Frevert D.K, editors. Mathematical Models in SmallWatershed Hydrology and Applications. Water Resour. Publ, P.O. Box2841, Littleton, Colorado 80161. 2002. pp. 391-464.
23
[24].Mishra S.K, Singh V.P, Sansalone J.J, Aravamuthan V. A modifiedSCS-CN method: characterization and testing. J. Water Resour. Manage. 2003;17: 37-68.
24
[25].Nash J. E, Sutcliffe J. V. River flow forecasting through conceptual models.part I- A discussion of principles. Journal of Hydrology. 1970; 10(3): 282-290.
25
[26].Adib A, Salarijazi M, Vaghefi M, Shooshtari M. M, Akhondali A. M. Comparison between GcIUH-Clark, GIUH-Nash, Clark-IUH, and Nash-IUH models. Turkish Journal of Engineering and Environmental Sciences. 2010; 34(2): 91-104.
26
[27].Adib A, Salarijazi M, Najafpour K. Evaluation of synthetic outlet runoff assessment models. Journal of Applied Sciences and Environmental Management. 2010; 14(3): 13-18.
27
[28].Adib A, Salarijazi M, Shooshtari M M, Akhondali A M. Comparison between characteristics of geomorphoclimatic instantaneous unit hydrograph be produced by GcIUH based Clark Model and Clark IUH model. Journal of Marine Science and Technology. 2011; 19(2): 201-209.
28
[29].Eidipour A, Akhondali A. M, Zarei H, Salarijazi M. Flood hydrograph estimation using GIUH model in ungauged karst basins (Case study: Abolabbas basin). TUEXENIA. 2016;36(3): 26-33.
29
ORIGINAL_ARTICLE
Application of the PHABSIM model in Explaining the Ecological Regime of the River in order to Estimate the Environmental Flow and Compare with Hydrological Methods (Case Study: Gharasoo River)
Lack of appropriate allocation Environmental Flow, has been disrupts the vital activities of aquatic organisms, reduces communication between ecosystems, access to suitable areas for spawning and migrating aquatic. In this research, the environmental demand of Gharasoo River at the siahab station hydrometry was investigated at the entrance to the Gorgan Gulf. Were evaluated of In order to obtain the ecological requirement of Gharasoo River in Golestan province hydrological methods of Tenant, Tessman, Arkansas, Physical Habitat Simulation Model (PHABSIM) for species Capoeta capoeta gracilis. The findings of this research show method of Tenant by taking 30 % annual average flow for the spring and summer seasons, 10 % annual average flow for autumn and winter seasons suggests respectively quantities 0/57 and 0/19 cms. Methods of Tessman, Arkansas and Physical Habitat Simulation model Provide estimates environmental water requirement in equal order 0/856, 1/22, 1/63 cms. Also, there was a lot of difference among between the results of estimating the minimum required water requirement for the river using hydrological methods and providing minimum habitat conditions for indicator species using habitat simulation method and as regards the ecological and habitat conditions of the river are a completely dynamic situation.
https://ije.ut.ac.ir/article_67660_553a1d39102d0d4e371ee24f1fbee0dd.pdf
2018-09-23
941
955
10.22059/ije.2018.253183.834
: Environmental Flow
Gharasoo River
Habitat Suitability
Habitat Simulation
hydrological methods
mohammad hasan
naderi
naderigau@gmail.com
1
Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources
AUTHOR
mehdi
zakerinia
a_zakerinia@yahoo.com
2
gorgan
LEAD_AUTHOR
meisam
salarijazi
meysam.salarijazi@gmail.com
3
gorgan
AUTHOR
[1]. Yan Y, Yang Z, Liu Q, Sun T. Assessing effects of dam operation on flow regimes in the lower Yellow River. Procedia Environmental Sciences. 2010; 2:507-16.
1
[2]. Lotfi, A. Guideline on rapid assessment of environmental features of rivers. Environment Protection Department of Iran Publication. 2012.[Persian]
2
[3]. Tabatabai MM, Nadushan RM, Hashemi S. Impact of hydrogeomorphic processes on ecological functions of brown trout habits. International Journal of Environmental Science and Technology. 2017;14(8):1757-70.
3
[4]. Arias M. I. E. Evaluating streamflow to characterize ecological functions of physical habitat in rivers. University of California, Davis; 2007.
4
[5]. Waddle T. PHABSIM for Windows user's manual and exercises. 2012.
5
[6]. Maddock I, Harby A, Kemp P, Wood PJ. Ecohydraulics: an integrated approach. John Wiley & Sons; 2013.
6
[7]. Blanckaert K, Garcia XF, Steiger J, Uijttewaal W. Ecohydraulics: linkages between hydraulics, morphodynamics and ecological processes in rivers. Ecohydrology. 2013; 6(4):507-510.
7
[8]. Poff NL, Richter BD, Arthington AH, Bunn SE, Naiman RJ, Kendy E, et al. The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards. Freshwater Biology. 2010; 55(1):147-70.
8
[9]. Tharme RE. A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. River research and applications. 2003;19(56):397-441.
9
[10]. Shokoohi A, Hong Y. Using hydrologic and hydraulically derived geometric parameters of perennial rivers to determine minimum water requirements of ecological habitats (case study: Mazandaran Sea Basin—Iran). Hydrological Processes. 2011; 25(22):3490-3498.
10
[11]. Bahukandi KD, Ahuja NJ. Building block methodology assisted knowledge-based system for environmental-flow assessment of Suswa River of Dehradun Dist., India: A reminiscent framework, International Research Journal of Environment Sciences.2013; 2(12):74-80.
11
[12]. Acreman M, Arthington A. H, Colloff M. J, Couch C., Crossman N. D, Dyer F, et al. Environmental flows for natural, hybrid, and novel riverine ecosystems in a changing world. Frontiers in Ecology and the Environment.2014; 12(8), 466-473.
12
[13]. Nia ES, Asadollahfardi G, Heidarzadeh N. Study of the environmental flow of rivers, a case study, Kashkan River, Iran. Journal of Water Supply: Research and Technology-Aqua. 2016; 65(2):181-94.
13
[14]. Booker DJ, Dunbar MJ. Application of physical habitat simulation (PHABSIM) modelling to modified urban river channels. River Research and Applications. 2004; 20(2):167-83.
14
[15]. Islam Md.S. Nature and limitations of environmental flow methodologies and its global trends. Journal of Civil Engineering. 2010; 38(2): 141-152.
15
[16]. Jowett IG, Hayes JW, Duncan MJ. A guide to instream habitat survey methods and analysis. Wellington: NIWA; 2008.
16
[17]. Oberdorff T, Pont D, Hugueny B, Porcher JP. Development and validation of a fish‐based index for the assessment of ‘river health’in France. Freshwater Biology. 2002; 47(9):1720-34.
17
[18]. Sedighkia M, Ayyoubzadeh S.A, Hajiesmaeli M. Investigation of Requirements for Estimation of the Environmental Flow in Rivers by Hydroacoustic Methods (Case Study: Delichay River located in Tehran Province). Journal of Ecohydrology. 2015; 2(3):289-300.
18
[19]. Sedighkia M, Ayyoubzadeh SA, Hajiesmaeli M. Modification of Tennant and Wetted Perimeter Methods in Simindasht Basin, Tehran Province. Civil Engineering Infrastructures Journal. 2017; 50(2):221-31.
19
[20]. Yi Y, Cheng X, Yang Z, Wieprecht S, Zhang S, Wu Y. Evaluating the ecological influence of hydraulic projects: A review of aquatic habitat suitability models. Renewable and Sustainable Energy Reviews. 2017; 68:748-62.
20
[21]. Nikghalb S, Shokoohi A, Singh VP, Yu R. Ecological regime versus minimum environmental flow: comparison of results for a river in a semi Mediterranean region. Water resources management. 2016; 30(13):4969-84.
21
[22]. Hashemi S, Majdzadeh M, Mosavi R. Range of biological currents of red tuna based on morphological and habitat parameters in the lar river basins. Journal of Natural Environment, Natural Resources of Iran. 2017; 69(3):865-880. [Persian]
22
[23]. Zhang Q, Xiao M, Liu CL, Singh VP. Reservoir-induced hydrological alterations and environmental flow variation in the East River, the Pearl River basin, China. Stochastic environmental research and risk assessment. 2014;28(8):2119-31.
23
[24]. Sedighkia M, Abdoli A, Ayyoubzadeh S.A, Ahmadi A.A, Gholizadeh M. Development of the native method of environmental flow in the rivers of the southern basin of Kaspian-Lar National Park. Journal of Ecology.2018; 43(3):543-560. [Persian]
24
[25]. Shokoohi AL, Amini MA. Introducing a new method to determine rivers’ ecological water requirement in comparison with hydrological and hydraulic methods. International Journal of Environmental Science and Technology. 2014; 11(3):747-56.
25
[26]. Tennant DL. Instream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries. 1976;1(4):6-10.
26
[27]. Abdi R, Yasi M. Evaluation of environmental flow requirements using eco-hydrologic–hydraulic methods in perennial rivers. Water Science and Technology. 2015;72(3):354-63.
27
[28]. Tessmann SA. Environmental Assessment. Technical Appendix E. Environmental use sector reconnaissance elements of the western Dakotas region of South Dakota study. Brookings, SD: South Dakota State University, Water Resources Research Institute. 1980.
28
[29]. Filipek SP, Keith WE, Giese J. Status of the Instream Flow Issue in Arkansas, 1987. Journal of the Arkansas Academy of Science. 1987;41(1):43-8.
29
[30]. Davis MM. Instream flow guidelines and protection of Georgia’s aquatic habitats. Georgia Institute of Technology; 2005.
30
[31]. Ahmadi-nadushan B, ST-Hilaire A, Berube M, Robichaud E, Thiemonge N, Bobee B. A review of statistical methods for the evaluation of aquatic habitat suitability for instream flow assessment. River Research and Applications.2006; 22:503-523.
31
[32]. Zamani M, Eagdari S, Zarei N. Study of the C.capoeta gracilis Habitat Suitability Index in the Kordan River. Fisheries Journal, Iranian Journal of Natural Resources.2015; 68(3):409-419. [Persian]
32
[33]. Tabatabaei N, Hashemzadeh I, Eagdari S, Zamani M. Determinative factors in habitat preference of Paracobitis iranica (Nalbant & Bianco 1998) in Kordan River, Namak lake watershed. Journal of Aquatic Ecology. 2014; 3(4):1-9. [Persian]
33
[34]. Gan K, McMahon T. Variability of results from the use of PHABSIM in estimating habitat area. River Research and Applications. 1990;5(3):233-9.
34
[35]. Vosoghi G, Mostajir B. Freshwater fish. Tehran University Press.2015. [Persian]
35
[36]. Asadi H, Sattari M, Eagdari S. Investigation of the determinants of selectivity and preferential habitat Capoeta capoeta gracilis (Keyserling 1891) in the Siahrood River. Iranian Journal of Fisheries Science. 2014; 23(3):1-9. [Persian]
36
[37]. Abdoli A. Iranian interior water fish. Museum of Nature and Wildlife of Iran. 1999. [Persian]
37
[38]. Thompson M. Minimum flow recommendations for the Wellington region.2015.
38
[39]. Keenan L, Thompson M and Mzila D. Freshwater allocation and availability in the Wellington region: state and trends. Wellington: NIWA;2012.
39
[40]. Hay J. Instream flow assessment for the lower Ruamahanga River. Prep. Gt. Wellingt. Reg. Counc. Cawthron Rep. 2008;1403.
40
[41]. Amini M, Shookohi A. Analytical solution Determination of the fracture point of the wetted environment graph - Discharge in a hydraulic method Determining the minimum environmental flow. Journal of Hydraulic.2014; 9(1):27-43. [Persian]
41
[42]. Zarakani M, Shookohi A, Pising V. Introducing a comprehensive ecological diet in the absence of data to determine the true environmental status of rivers. Iranian Water Resources Research Journal. 2017; 13(2):140-153. [Persian]
42
[43]. Karimi SS, Yasi M, Eslamian S. Use of hydrological methods for assessment of environmental flow in a river reach. International Journal of Environmental Science and Technology. 2012; 9(3):549-58.
43
ORIGINAL_ARTICLE
Evaluation of the Efficiency of Statistical Downscaling Model (SDSM) In Simulation and Forecast of Climatic Parameters (Case Study: Karaj Synoptic Station)
The increase in the world's population, the use of more than fossil fuels, landuse change, the increasing expansion of industrial activities to provide the welfare and needs of the planet's population has led to gradual changes in the climate after the Industrial Revolution The Earth is the most significant of which is the increase in the average temperature in Korea, the increase of extreme climatic phenomena such as floods,storms,rising sea levels, melting of polar ice and Drought.In this research, SDSM model was used for quantitative estimation and investigation of climate change in Karaj region. The simulation results, on average, in the scenario A2, in the first periods(2020-1999),second(2021-2050) and third(2051-2080) for rainfall were about 0.1, 0 and 0.2mm in comparison with the base period and in the case of temperature is about 0.1, 0.4 and 0.2C, respectively, relative to the base period of increase. Under scenario B2, the time periods mentioned for rainfall were about 0, 0.1 and 0.2 millimeters, respectively, and about 0.2,0.1 and 0.1 centigrade, respectively Shows the increase relative to the base period.Changes in rainfall will lead to significant changes in the quality and quantity of water resources, which require careful planning in order to utilize water resources.
https://ije.ut.ac.ir/article_67661_0e9141c06625106fdf8c2d1825fada00.pdf
2018-09-23
957
968
10.22059/ije.2018.254290.847
climate change
Climate scenarios
simulation
SDSM model
Hossein
Yousefi
hosseinyousefi@ut.ac.ir
1
مدیر گروه علوم و فناوریهای محیطی، دانشکده علوم و فنون نوین دانشگاه تهران
LEAD_AUTHOR
leili
amini
leili.amini@ut.ac.ir
2
Mac Student in Ecohydrology, University of Tehran, Iran
AUTHOR
leila
ghasemi
ghasemi.leila@ut.ac.ir
3
Mac Student in Ecohydrology, University of Tehran, Iran
AUTHOR
nasibeh
amrai
nasibeh.amrai@ut.ac.ir
4
Mac Student in Ecohydrology, University of Tehran, Iran
AUTHOR
[1]. Azizabadi farahan M, Bakhtiari B, Ghaderi K, Rezapour M. Assessment the Effect of Climate Change on Severity- Duration-Frequency Curves Of Drought in Ghareh Sou Basin Using Detailed Functions, Journal of Iranian soil and soil research. 2017; 47(4): 743-754. [Persian]
1
[2]. Rezaei M, Nehtani M, Abkar A, Rezaei M, Mirkazehi Rigi M. Erformance evaluation of statistical downscaling model (Sdsm) in the prediction of temperature parameters in the dry climate and Hyper (Case Study: Kerman and Bam). Journal of Watershed Management. 2014;5:10. [Persian]
2
[3]. Shiaae beighi A, Abbaspour M, Soltanieh M, Hosseinzadeh F, Abedi Z. Assessment of climate change and its impact on the performance and fuel consumption of Iran's thermal power plants in the next decade. Journal of Science and technology of the environment. 2014;16(2). [Persian]
3
[4]. Yaaghubi, M. Masahbovani, A. Compare and evaluate different sources of uncertainty in studying the effects of climate change on runoff of semi-arid basins (Case Study: Heart River Basin large-Yazd), Iranian Water Resources Research. 2015;11(3):113-130. [Persian]
4
[5]. Tirgarfakheri, F. Arezumandi, L. Applications of downscaling in GCMS to create a map of rainfall in the southern coast of the Caspian, Third International Symposium on Environmental and Water Resources Engineering. 2015;1-10. [Persian]
5
[6]. Taei Samiromi S, Moradi H, Khodagholi M. Simulation and forecasting of climatic variables by multiple linear model Sdsm and General Circulation Models (Case Study: Watershed Bar Nishapur). Journal of humans and the environment. 2014;28:1393. [Persian]
6
[7]. Sayari N, Alizadeh A, Bannayan Awal M, Farid Hossaini A, Hesami Kermani M.R. Comparison of two GCM models (HadCM3 and CGCM2) for the prediction of climate parameters and crop water use under climate change (Case Study: Kashafrood Basin). Journal of Water and Soil. 2011; 25(4): 912-925. [Persian]
7
[8]. Tavakol-Davani,H. Nasserib ,a M. Zahraie, B. Improved statistical downscaling of daily precipitation using SDSM platform and data-mining methods. International Journal of Climatology,1-18. [Persian]
8
[9]. SadatAshofteh P ,MasahBoani A. Effect of Climate Change on Maximum Discharge: Case Study, Aydoghmoos Basin, East Azarbaijan, Journal of Agricultural Sciences and Technology. 2010. 14; 25-39. [Persian]
9
[10]. Arun Mondal n, Deepak K, Sananda K. Change in rainfall erosivity in the past and future due to climate change in the central part of India, International SoilandWaterConservationResearch4,pp. 2016;186–194.
10
[11]. Zhang Y, You Q, Chen Ch, Ge J. Impacts of climate change on streamflows under RCP scenarios: A case study in Xin River Basin, China. Atmospheric Research. 2016; 178–179
11
[12]. Garnaud C, Sushama L. Biosphere-climate interactions in a changing climate over North America. Journal of Geophysical Research: Atmospheres. 2014;1091-1108.
12
[13]. Wetterhall F, Bardossy A, Chen D, Halldin S, Xu C-Y. Daily precipitation-downscaling techniques in three Chinese regions. WATER RESOURCES RESEARCH. 2006; (42)11423,1-13.
13
[14]. Wilby, R.L. Dawson, C.W. Barrow,E.M. sdsm -a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software. 2002;17,147-159.
14
[15]. Dibike, Y.b. and P. Coulibaly. Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. Journal of Hydrology. 2005;307: 145-163.
15
[16]. Zia Hashmi,M.shamseldin,A. Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Environ Res Risk Assess. 2011;25,475-484.
16
[17].Tavasoli A. Intra-Storm runoff coefficient simulation using the components of rainfall in the watershed bar Nishapur, Journal of Watershed Management Sciences and Engineering. 2010;10(4): 21-33. [Persian]
17
[18]. Moriasi D N, Arnold JG, Van Liew M. W, Bingner R. L, Harmel, R. D, and Veith, T. L. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE. 2007; 50(3), 900-855.
18
[19]. Taei Samiromi S, Moradi H, Khodagholi M. Selection of general circulation model and appropriate scenario for studying the effects of climate change in watershed Bar Neishabour. The 2nd National Conference on Climate Change and its Impact on Agriculture and the Environment, Orumiyeh, Collections of articles. 2013; 28-32. [Persian]
19
ORIGINAL_ARTICLE
Modelling the effect of water fall in the river level on unsteady groundwater flow in leaky aquifer by separation of variables
In order to model groundwater flow, numerical and analytical methods can be utilized. In this paper, the effects of different parameters on leaky aquifer were investigated using mathematical model and separation of variables method. This aquifer is located adjacent to the river and the flow rate falls across the border. Comparison of hydraulic head changes shows that over the time the water level changes decreases in the aquifer and the aquifer adapts itself to the new conditions. Groundwater level decrease with rises in hydraulic conductivity. Reducing hydraulic conductivity has a greater effect than increasing it on the aquifer. Also, the groundwater head rises by increasing the recharge rate and over time, these changes are more evident. Outflow changes are greater than inflow changes. In addition, the presented analytical solution is compared with those results obtained from MODFLOW. This comparison showed that the analytical solution presented in this research is very efficient.
https://ije.ut.ac.ir/article_67662_33c943a07cf3bb1ee0bd91d6438dfaf3.pdf
2018-09-23
969
976
10.22059/ije.2018.254655.852
surface water – aquifer interaction
separation of variables
leaky aquifer
water level fall
Iraj
Saeedpanah
saeedpanah@znu.ac.ir
1
Assistant Professor Department of Civil Engineering ,University of Zanjan
LEAD_AUTHOR
Somayeh
Mohammadzade Roofchaee
somayeh.mohamadzade@znu.ac.ir
2
2- M.Sc. student Hydraulic Structures, Civil Engineering ,University of Zanjan
AUTHOR
[1]. Winter, T. C. Relation of streams, lakes, and wetlands to groundwater flow systems. Hydrogeology Journal. 1999; 7:28–45
1
[2]. Yang, Y.S., Wang, L. A review of modeling tools for implantation of the EU water framework directive in handling diffuse water pollution. Water Resources Management. 2010; 24:1819–1843
2
[3]. Courbis, A. L., Vayassade, B., Martin, C., Didon-Lescot, J.F. Modelling and simulation of a catchment in order to evaluate water resources. Global NEST Journal. 2008; 10(3): 301-309.
3
[4]. Ma, S., Kassinos, S.C., Kassinos, D.F., Akylas, E. Modeling the impact of water withdrawal schemes on the transport of pesticides in the Kouris Dam (Cyprus). Global NEST Journal. 2008; 10(3): 350-358.
4
[5]. Boufadel, M. C., Peridier, V. Exact analytical expressions for the piezometric profile and water exchange between stream and groundwater during and after a uniform rise of the stream level. Water resources research. 2002; 38(7): 1-6.
5
[6]. Hussein, M., Schwartz, F.W. Modeling of flow and contaminant transport in coupled stream–aquifer systems. Journal of Contaminant Hydrology. 2003; 65: 41–64.
6
[7]. Singh, S.K. Aquifer response to sinusoidal or arbitrary stage of semipervious stream. Journal of Hydraulic Engineering. 2004; 130(11): 1108-1118.
7
[8]. Kim, K.Y., Kim, T., Kim, Y., Woo, N.C. A semi-analytical solution for groundwater responses to stream-stage variations and tidal fluctuations in a coastal aquifer. Hydrological Process. 2007; 21(5): 665–674.
8
[9]. Bansal, R. K., Das, S. K. Analytical solution for transient hydraulic head, flow rate and volumetric exchange in an aquifer under recharge condition. Journal of Hydrology and Hydromechanics. 2009; 57(2): 113-120.
9
[10]. Guo, H. P., Jiao, J.J., Li, H. L. Groundwater response to tidal fluctuation in a two-zone aquifer. Journal of Hydrology. 2010; 381:364–371.
10
[11]. Telogloua L.S, Bansal, R k. Transient solution for stream–unconfined aquifer interaction due to time varying stream head and in the presence of leakage. Journal of Hydrology. 2012; 428: 68–79.
11
[12]. Kashaigili, J. J., Mashauri D. A., Abdo, G. Groundwater management by using mathematical modeling: case of the Makutupora groundwater basin in dodoma Tanzania. Botswana Journal of Technology. 2003; 12(1):19–24.
12
[13]. Palma, H. C., Bentley, L. R. A regional-scale groundwater flowmodel for the Leon–Chinandega aquifer, Nicaragua. Hydrogeology Journal. 2007; 15:1457–72.
13
[14]. Budge, T.J., Sharp, Jr. JM. Modeling the usefulness of spatial correlation analysis on karst systems. Ground Water. 2009; 47(3):427–37.
14
[15]. Xu, X., Huang, G., Zhan, H., Qu, Z., Huang, Q. Integration of SWAP and MODFLOW-2000 for modeling groundwater dynamics in shallow water table areas. Journal of Hydrology. 2012; 412:170–181.
15
[16]. Saeedpanah I, GolmohamadiAzar R, New Analytical Solutions for Unsteady Flow in a Leaky Aquifer between Two Parallel Streams. Water Resources Management. 2017; 31(7): 2315–2332.
16
[17]. Srivastava, Kirti;Serrano, Sergio E.; Workman, S. R. Stochastic modeling of transient stream aquifer interaction with the nonlinear Boussinesq equation. Journal of Hydrology. 2005; 328: 538-547.
17
ORIGINAL_ARTICLE
Prediction of drought condition during 2018-2037 period under Climate Change Approach (Case study: Ilam and Dehloran Stations)
Drought phenomena may cause unpredictable changes under influence of climate change and there are indexes for its evaluation. In this research, firstly, base period’s drought (1998-2017) was evaluated in 3, 6, 12 and 24-month time series in synoptic stations of Ilam and Dehloran, located in Ilam province, through using monthly precipitation data. Then, monthly precipitation of future period (2018-2037) were studied through using daily data of precipitation, minimum temperature, maximum temperature and radiation via using downscaling LARS-WG Model under Hadcm3 General Circulation Model and A2 and B1 Regional Scenarios. Then, SPI drought index was evaluated for future period in desired time series. Results of drought evaluation in base period in Ilam Station represented that 2008-2014 period had been a relatively humid period. It also represented timid period in Dehloran Station at beginning of period. Evaluation of drought in future period based on A2 and B1 Scenarios presented there will be a mostly drought period in Ilam Station between 2025 to 2035. Also, there will be a complete drought period in Dehloran Station from 2019 to 2021. Also, results represented that duration of drought and timid periods are increasing and their severity will be decreased by increase of statistical period.
https://ije.ut.ac.ir/article_67663_73484b2d047066c46c3b27038dae2647.pdf
2018-09-23
977
991
10.22059/ije.2018.256186.866
climate change
SPI Index
LARS-WG Model
Hadcm3 Model
Eghbal
Norozi
eghbalnorozi@ut.ac.ir
1
University of Tehran
AUTHOR
Noredin
Rostami
n.rostami@ilam.ac.ir
2
Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University
LEAD_AUTHOR
Mohammad Hossein
Jahangir
mh.jahangir@ut.ac.ir
3
Assistant Professor, Faculty of New Sciences & Technologies, University of Tehran
AUTHOR
[1]. Van Pelt SC, Swart RJ. Climate change risk management in transnational river basin: The Rhine. Water Resource Management, 2011; 25(1): 3837-3861.
1
[2]. IPCC (Intergovernmental Panel on Climate Change). Summary for policy makers. In: IPCC. Climate change: The physical Science basic, Contribution of working group first to the Fourth assessment report of the intergovernmental panel on climate change, Cambridge university press, 2007: 450p.
2
[3]. Quevauviller P. Adapting to climate change: reducing water-related risks in Europe – EU policy and research considerations. Environmental Science and Policy, 2011; 14(7): 722-729.
3
[4]. Parvaneh B, Dargahian F, Shiravand H. Prediction of Drought in Lorestan province during 2011-2030 by downscaling 4 GCM models. Quarterly Geographical Journal of Territory, 2015; 12 (45):1-13. [Persian].
4
[5]. Golmohammadi M, Massah Bavani A. The Perusal of Climate Change Impact on Drought Intensity and Duration, Journal of Water and Soil, 2011; 25(2): 315-326. [Persian].
5
[6]. Kiem AS, Austin EK. Drought and the future of rural communities: Opportunities and challenges for climate change adaptation in regional Victoria, Australia. Global Environmental Change, 2013; 23:1307-1316.
6
[7]. Philip GO, Babatunde JA, Gunner L. Impacts of climate change on hydro-meteorological drought over the Volta Basin, West Africa. Global and Planetary Change, 2017; 155; 121-132.
7
[8]. Vidal JP, Wade S. A multimodel assessment of future climatological droughts in the United Kingdom. International Journal of Climatology, 2009; 29(14): 2056-2071.
8
[9]. Babaeian E, Nagafineik Z, Zabolabasi F, Habibie M, Adab H, Malbisei S. Climate Change Assessment over Iran During 2010-2039 by Using Statistical Downscaling of ECHO- G Model, 2010; 7(16): 135-152. [Persian].
9
[10]. Abdul Hosseini M, Eslamian S, Musavi SF. Analysis of variation of drought socio-economic characteristics and the effect of climate change, First National Conference on Meteorology and Water Management, Tehran, University of Technology, Department of Irrigation Engineering. 2010: 1-10. [Persian].
10
[11]. Dastorani MT, Massah Bavani A, Poormohammadi S, Rahimian MH. Assessment of potential climate change impacts on drought indicators (case study: Yazd Station, Central Iran), Journal of Desert, 2011; 16(2):159-167. [Persian].
11
[12]. Abbasi F, Asmari M. Forecasting and Assessment of Climate Change over Iran During Future Decades by Using MAGICC-SCENGEN Model, Journal of Water and Soil, 2011; 25(1): 70-83. [Persian].
12
[13]. Vrochidou AE, Tsanis IK, Grillakis MG, Koutroulis AG. The impact of climate change on hydro meteorological droughts at a basin scale. Journal of Hydrology, 2013; 476(8): 290-301.
13
[14]. Salehpour jam A, Mohseni Saravi M, Bazrafshan J, Khalighi S. Investigation of Climate Change Effect on Drought Characteristics in the Future Period using the HadCM3 model (Case Study: Northwest of Iran), Journal of Range and Watershed Management, 2015; 67(4): 537-548. [Persian].
14
[15]. Nikbakht Shahbazi A. Standard Precipitation Index (SPI) analysis in Karoon 3 Watershed under climate change, Journal of Science Water Engineering, 2013; 3(8): 83-98. [Persian].
15
[16]. Hoseinizade A, Seyed Kaboli H, Zarei H, Akhond Ali AM. The Intensity and Return Period of Drought under Future Climate Change Scenarios in Dezful Iran, Journal of Irrigation Science Engineering, 2016; 39(1): 33-43. [Persian].
16
[17]. Sajjad Khan M, Coulibaly P, Dibike Y. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology, 2006; 319(1-4): 357-382.
17
[18]. McKee TB, Doesken NJ, Kleist J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, 1996; 17(22):179-183.
18
[19]. Thom HCS. A note on the gamma distribution, Weather Review, 1958; 86(4):117-122.
19
[20]. Abramowitz, M., Stegun, I.A. Handbook of Mathematical Functions. Dover Publications, 1965; New York.
20
[21]. Edwards DC, McKee TB. Characteristics of 20th century drought in the United States at multiple time scales. Colorado State University, Climatology Report Number 97–2, Fort Collins, Colorado, 1997.
21
[22]. Pirnia A, Golshan M, Bigonah S, Solaimani Karim. Investigating the drought characteristics of Tamar basin (upstream of Golestan Dam) using SPI and SPEI indices under current and future climate conditions, Journal of Ecohydrology, 2018; 5(1); 215-228. [Persian].
22
[23]. Bazrafshan O, Mohseni Saravi M, Malekian A, Moeini A. A study on drought characteristics of Golestan Province using Standardized Precipitation Index (SPI). Iranian Journal of Range and Desert Reseach, 2011; 18 (3):395-407. [Persian].
23
ORIGINAL_ARTICLE
Assessment and zoning of groundwater quality of Bojnord plain during drought and wet periods with using SPI, RAI and PN indices.
One of the major consequences of climate change is the increasing severity of drought. Drought is a recurring natural phenomenon that is associated with a shortage of available water resources in a large area over a given period of time. In recent years, the increasing frequency of occurrence of extreme climatic phenomena such as floods and droughts, along with global warming evidence, has led to an increase in attention to climatic issues. In this research, by preparing climatic statistics of two stations in Bojnourd and Assadi, the drought condition of Bojnoord plain has been investigated according to drought indices SPI, RAI and PN. Then, using Piper and Wilcox diagram, the quality of groundwater resources has been compared during drought and wet periods. The results of the survey indicate that the Bojnourd plain has been in drought situations in recent years. The results show that the quality of water resources during the wet period was mostly mixed and sweet water, but in the drought period, the water type was more than the mixed and saline water. The results also show that in the drought period more than 50 percent of wells in saltwater conditions were unsuitable for agriculture.
https://ije.ut.ac.ir/article_67664_9fe7d83b398ec87d8dc1be90bf4e3acd.pdf
2018-09-23
993
1005
10.22059/ije.2018.257381.875
zoning
Drought
Groundwater
Bojnourd Plain
Hossein
Yousefi
hosseinyousefi@ut.ac.ir
1
مدیر گروه علوم و فناوریهای محیطی، دانشکده علوم و فنون نوین دانشگاه تهران
AUTHOR
Abdolreza
Kashki
r.kashki@yahoo.com
2
Dept. of Physical Geography و Faculty of Geographic & Environmental Sciencesو Hakim Sabzevari Universityو Sabzevar, Iran
LEAD_AUTHOR
mokhtar
karami
m.karami08@yahoo.co.uk
3
Dept. of Physical Geography, Faculty of Geographic & Environmental Sciences ,Hakim Sabzevari University , Sabzevar, Iran
AUTHOR
ahmad
hosseinzadeh
ahmad.ut@gamil.com
4
Ph.D. in Clinical Sciences, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar
AUTHOR
elyas
reyhani
elyas.reyhani@ut.ac.ir
5
faculty of new sciences and technologies,university of tehran
AUTHOR
[1]. Kelanki M and Karandish F. Forecasting the long-term effects of climate change on climatic components in the region wet, journal of Water and Irrigation Engineering,2015,20(5),131-148.[Persian]
1
[2]. Navare A. An Analysis on International Society Practice toward the Climate Change. Environmental research.2015; 5 (9), 47-58.[Persian]
2
[3]. Jamali Z, Khoorani A. Impacts of climate change on extreme precipitation events in arid (Bandar Abbas) and semi-arid (Shahrekord) stations in Iran. Natural Environment Change. 2015 Jul 1; 1(1):85-94. [Persian]
3
[4]. farajzadeh m and ahmadian g. Temporal and Spatial Analysis of Drought with use of SPI Index in Iran. Journals management system.2014; 3(4), 1-16. [Persian]
4
[5]. HOSEINIZADE A, SEYED KH, ZAREI H, AKHON AA. The Intensity and Return Period of Drought under Future Climate Change Scenarios in Dezful, Iran. Journal of irrigation science and engineering.2016; 39(1), 33-43.[Persian]
5
[6]. ASADZADEH F, KAKI M, SHAKIBA S, RAEI B. Impact of drought on grounwater quality and groundwater level in QORVEH-CHARDOLI plain. Iran-water resources research.2016; 12(3), 153-165.[Persian]
6
[7]. Jahangir M, Norozi e. Numerical comparison of RAI and PNPI meteorological indices to assess and quantify the drought situation in Khuzestan province. Journal of echohydrology, 2017; 4(3), 923-930.[Persian]
7
[8]. Naserzade M, Ahmadi E. Evaluation of the performance of meteorological drought indicators in drought evaluation and zoning in Qazvin province.Researches in Geographical Sciences; 2013, 12(27): 141-162.[Persian]
8
[9]. Borna R ,Azimi F ,Saeidi dehaki N. Comparison of SIAP, PN, RAI indicators In the study of drought in Khuzestan province With emphasis on Abadan and Dezful stations, Natural History Quarter,2010;3(9),77-88.[Persian]
9
[10]. ANSARI H, DAVARI K, Sanaeinezhad SH. Drought monitoring with new precipitation and evapotranspiration index based on Fuzzy Logic. Journal of Water and Soil,2010;24(1),38-52.
10
[11]. Bazrafshan O, MOHSENI SM, Malekian A, Moeini A. A study on drought characteristics of Golestan province using standardized precipitation index (SPI). Journal of rangeland and desert research, 2011; 18(3), 395-407.
11
[12]. Yosefi H, Nohegar A, KHosravi Z, Farahani M. Management and zonation Drought using SPI and RDI indices (case study: Markazi province),Journal of echohydrology,2015;2(3):337-344. [Persian]
12
[13]. Pramudya Y ,Onishi T. Assessment of the Standardized Precipitation Index (SPI) in Tegal City, Central Java, Indonesia, IOP Conf. Series, Earth and Environmental Science, 2018 Mar;129(1):12-19.
13
[14]. Roshan M, Karimi V, Abjar J. Investigation of meteorological drought Indices in Mazandaran synoptic Stations,2011;2(5),15-25.[Persian]
14
[15]. Moasedi A, Qabaei M. Modification of Standardized Precipitation Index (SPI) Based on Relevant Probability Distribution Function, Journal of Water and Soil, 2011; 25(5):1206-1216.[Persian]
15
[16]. Khandouzi F ,Zangane A ,Zamani A ,Zhahamat Y. Survey of Hydro-geochemical Quality and Health of Groundwater in Ramian, Golestan Province, Iran, Journal of Health Research in Community,2015,1(3):41-52.[Persian]
16
[17]. Mooney PH. Toward a class analysis of Midwestern agriculture. Rural Sociology. 1983 Dec 1; 48(4):563.
17
[18]. Sikdar P, Sarkar S, Palchoudhury k, S. Geochemical evolution of groundwater in the Quaternary aquifer of Calcutta and Howrah, India, Journal of Asian Earth Sciences,2008;19(5),579-594.
18
[19]. Nadiri A , Asghari A , Frank T-C, Fijani E. Hydrogeochemical analysis for Tasuj plain aquifer, Iran, Journal of Earth System Science,2013;122(4),1091-1105.
19
[20]. Giglo B, Farid B, Najafi Nejad A, Moqani V, Qiasi A. Evaluation of water quality variation of Zarringol river, journal of Water and Soil Conservation Studies,2013;20(1),77-95.[Persian]
20
ORIGINAL_ARTICLE
A review of the effect of nanofluids to reduce water loss and improve thermal properties in cooling towers
In most of the factories, one of the most important and practical devices is the type of cooling towers which are used to release extra heat from processes in various industries to the environment. This study is an overview of novel ways on the effect of different type’s nanofluids on the thermal performance and reduce the amount of flowing water down the cooling tower. Nanofluids can improve thermo physical properties such as heat capacity& thermal conductivity coefficient and increase density & viscosity in comparison to base fluid. The dispersion of the nanoparticle increases the surface tension of the nanofluid and increases the resistance to water evaporation.So in this paper study influence of ZnO/water, nonporous graphene, AL2O3/ water, TiO2/water, CuO/water with the different concentration in order to improve cooling tower performance. It was found that by using nanofluids, cooling range, cooling tower characteristic (TC), volumetric heat transfer coefficient and efficiency are enhanced in comparison to water. For example, TC enhanced by 21.5% and 22.5% for ZnO/water nanofluid with concentration of 0.02 wt% and 0.05 wt%, respectively. In continue, results of sensitivity analyses that has been carried out in investigations, are discussed.
https://ije.ut.ac.ir/article_67720_c33ad0c3c084377929c832eb9ee9adf5.pdf
2018-09-23
1007
1015
10.22059/ije.2018.257411.874
Cooling tower
Nano Fluid
Thermal performance
cooling range
Water loss
Fatemeh
Razi Astaraei
razias_m@ut.ac.ir
1
استادیار دانشکدۀ علوم و فنون نوین دانشگاه تهران
LEAD_AUTHOR
seyed ali
Mousavi
s.a.mousavi74@ut.ac.ir
2
Department of Renewable Energies and Environment, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
AUTHOR
[1]. Askari S, Lotfi R, Seifkordi A, Rashidi AM, Koolivand H. A novel approach for energy and water conservation in wet cooling towers by using MWNTs and nanoporous graphene nanofluids. Energy conversion and management. 2016 Feb 1;109:10-8.
1
[2]. Zhai Z, Fu S. Improving cooling efficiency of dry-cooling towers under cross-wind conditions by using wind-break methods. Applied Thermal Engineering. 2006 Jul 1;26(10):1008-17.
2
[3]. Imani-Mofrad P, Saeed ZH, Shanbedi M. Experimental investigation of filled bed effect on the thermal performance of a wet cooling tower by using ZnO/water nanofluid. Energy Conversion and Management. 2016 Nov 1;127:199-207.
3
[4]. Xie X, Zhang Y, He C, Xu T, Zhang B, Chen Q. Bench-Scale Experimental Study on the Heat Transfer Intensification of a Closed Wet Cooling Tower Using Aluminum Oxide Nanofluids. Industrial & Engineering Chemistry Research. 2017 May 12;56(20):6022-34.
4
[5]. Goodarzi, M., Kiasat, M., Influence of nanoparticle immersed in water on Thermal performance of wet cooling tower. 1th tajhizatconf., Dec. 2013. Tehran, Iran. [Persian].
5
[6]. Babu JR, Kumar KK, Rao SS. State-of-art review on hybrid nanofluids. Renewable and Sustainable Energy Reviews. 2017 Sep 1;77:551-65.
6
[7]. Esfe MH, Alirezaie A, Rejvani M. An applicable study on the thermal conductivity of SWCNT-MgO hybrid nanofluid and price-performance analysis for energy management. Applied Thermal Engineering. 2017 Jan 25;111:1202-10.
7
[8]. Yarmand H, Gharehkhani S, Ahmadi G, Shirazi SF, Baradaran S, Montazer E, Zubir MN, Alehashem MS, Kazi SN, Dahari M. Graphene nanoplatelets–silver hybrid nanofluids for enhanced heat transfer. Energy conversion and management. 2015 Aug 1;100:419-28.
8
[9]. Rao RV, Patel VK. Optimization of mechanical draft counter flow wet-cooling tower using artificial bee colony algorithm. Energy Conversion and Management. 2011 Jul 1;52(7):2611-22.
9
[10]. Atarzadeh MA, Rasouli S, Mehmandoust B. Numerical Analysis the Equations of Heat and Mass Transfer in Cooling Towers. Department of Mechanical Engineering, Islamic Azad University, Khomeini Shahr Branch, Khomeini Shahr, Iran. 2015.
10
[11]. Alavi SR, Rahmati M. Experimental investigation on thermal performance of natural draft wet cooling towers employing an innovative wind-creator setup. Energy conversion and management. 2016 Aug 15;122:504-14.
11
[12]. Azad AV, Azad NV. Application of nanofluids for the optimal design of shell and tube heat exchangers using genetic algorithm. Case Studies in Thermal Engineering. 2016 Sep 1;8:198-206.
12
[13]. Pak BC, Cho YI. Hydrodynamic and heat transfer study of dispersed fluids with submicron metallic oxide particles. Experimental Heat Transfer an International Journal. 1998 Apr 1;11(2):151-70.
13
[14]. Lim S, Horiuchi H, Nikolov AD, Wasan D. Nanofluids alter the surface wettability of solids. Langmuir. 2015 May 21;31(21):5827-35.
14
[15]. Bhuiyan MH, Saidur R, Amalina MA, Mostafizur RM, Islam AK. Effect of nanoparticles concentration and their sizes on surface tension of nanofluids. Procedia Engineering. 2015 Jan 1;105:431-7.
15
ORIGINAL_ARTICLE
Investigating of Groundwater Head-Loss Impact on Soil Erosion Process and Formation of Dust Phenomenon
The purpose of this research is to analyze the decrease of surface moisture and source of dusts in Hamoon-Hirmand Basin of Sistan and Baluchestan province. In order to achieve the objectives of this paper, WEAP software was used to simulate subsurface flow drops and then, by applying water resource management scenarios, the subsurface flow loss rate was simulated by 2031. Using the hierarchical analysis method, the best option among the scenarios was selected and the area under study under this scenario at the end of the year is 29 cm below the subsurface flow rate and the total unsecured amount for various criteria such as drinking, agriculture And the environment is equal to 804.183 million cubic meters. The wind speed in the study area is greater than the velocity threshold of the particles (more than 400 cm / s), so the area is capable of producing dust. The HYDRYS-1D model has been used to analyze the soil moisture content reduction in this paper. The obtained results indicate a decrease in soil moisture content in each year. It was concluded that the reduction of soil moisture content is closely related to the subsurface flow rate and the potential for dust
https://ije.ut.ac.ir/article_67721_e77bd036f4ae04e4aeaa46e49b532306.pdf
2018-09-23
1017
1035
10.22059/ije.2018.241942.728
Groundwater Head Loss
Soil moisture
Dust
Hamoon Hirmand Basin
gholamreza
azizyan
g.azizyan@eng.usb.ac.ir
1
Assistance Professor, Faculty of Engineering, University of Sistan and Baluchestan, zahedan, Iran
LEAD_AUTHOR
Seyed Arman
Hashemi Monfared
hashemi@eng.usb.ac.ir
2
دانشیار، دانشکدۀ مهندسی شهید نیکبخت، گروه مهندسی عمران، دانشگاه سیستان و بلوچستان
AUTHOR
Amirhosein
Javan Mohasel
ajavan.civil@gmail.com
3
university of Sistan and Baluchestan
AUTHOR
Mohsen
Dehghani Darmian
mohsen.dehghani@pgs.usb.ac.ir
4
Ph. D. Candidate, Shahid Nikbakht Faculty of Engineering, Civil Engineering Department, Sistan and Baluchestan, Zahedan
AUTHOR
[1]. Subramanya, K. Engineering Hydrology. 1nd ed. Mashhad: Hashemi, S, R; 2003. [Persian]
1
[2]. Kardvani, P. The desert (salt) of central Iran and its neighboring areas. 1nd ed. Tehran: Tehran University Press; 2007.[Persian]
2
[3]. Elmore, A. Kaste, J. Okin, G. Fantle, M. Groundwater influences on atmospheric dust generation in deserts. Journal of Arid Environments. 2008; 72(1): 1753– 1765.
3
[4]. Maki T, Hara K, Iwata A, Lee KC, Kawai K, Kai K, Kobayashi F, Pointing SB, Archer S, Hasegawa H, Iwasaka Y. Variations in airborne bacterial communities at high altitudes over the Noto Peninsula (Japan) in response to Asian dust events. Atmospheric Chemistry and Physics. 2017 Oct 9;17(19):11877-97.
4
[5]. Maki T, Kurosaki Y, Onishi K, Lee KC, Pointing SB, Jugder D, Yamanaka N, Hasegawa H, Shinoda M. Variations in the structure of airborne bacterial communities in Tsogt-Ovoo of Gobi desert area during dust events. Air Quality, Atmosphere & Health. 2017 Apr 1;10(3):249-60.
5
[6]. Bi J, Huang J, Shi J, Hu Z, Zhou T, Zhang G, Huang Z, Wang X, Jin H. Measurement of scattering and absorption properties of dust aerosol in a Gobi farmland region of northwestern China–a potential anthropogenic influence. Atmospheric Chemistry and Physics. 2017 Jun 15;17(12):7775.
6
[7]. Zangane, M. Meteorology of dust storms in Iran. Two Applied Meteorological Quarterly.2014; (1): 1-12. [Persian]
7
[8]. Yasrebi, S. Unsaturated soil mechanics. 1nd ed. Tehran: Knowledge Shape; 2007. [Persian]
8
[9]. Abbasi, H, R. Baranizade, M, R. Khaksarian, F. Gohardost, A. A simple method for determining the susceptibility of land to winding on the basis of field data of the sampling center of Sistan. Third National Conference on Wind Erosion and Dust Hurricanes.2013; (1):1-10. [Persian]
9
[10]. Maher, B, A. Prospero, J, M. Mackie, D. Gaiero, D. Hesse, P, P. Balkanski, F. Global connections between aeolian dust, climate and ocean biogeochemistry at the present day and at the last glacial maximum. Earth-Science Reviews. 2010; (1): 61–97.
10
[11]. Servati, M, R. Yosefiroshan, M, R. Issues related to the transfer of sand and fine particles (dust) (by wind in dry and low water areas. Geographical Information (Sepehr).2011; (21):16-35. [Persian]
11
[12]. Kyaniselmy, E. Honarbakhsh, A. Abdolahy, KH. Sensitivity analysis of soil moisture model for continuous simulation in Beheshtabad Basin. Journal of Ecohydrology. 2017; (4): 1117-1127. [Persian]
12
[13]. Pointing, S, B. Belnap, J. Disturbance to desert soil ecosystems contributes to dust-mediated impacts at regional scales, Biodivers Conserv. 2014; 1659–1667.
13
[14]. Ragabpor, H. Mahmodabadi, M. Study of the severity of wind erosion in two soils with different particle size. International Conference on Plant, Water, Soil, and Soil Modeling.2013; ;(2):18-28. [Persian]
14
[15]. Salehi, M, H. Sfandyarpor, A. Mohajer, R. Bagheri, M. Water and soil protection. 1nd ed. Tehran: Payam Noor university; 2013.
15
[16]. Ghobadyan, R. Bahrami, Z. DabaghBaghry, S. Application of management scenario in prediction of groundwater fluctuations with MODFLOW conceptual and mathematical model (Case Study: Khazal Plain - Nahavand). Journal of Ecohydrology. 2016; (3): 303-319. [Persian]
16
[17]. Mohammadi, A. Delbari, M. Chari, M, M. Comparison of SWAP and HYDRUS-1D models in simulating water movement and salt concentration in soil. National Conference on Irrigation and Reduction of Evaporation.2013 ;( 12):12-22. [Persian]
17
[18]. Asar, A. Derakhshannegad, Z. Soltanimohammadi, A. Goshe, M. Soil moisture simulation with HYDRUS-1D model in wheat cultivation conditions. Journal of Irrigation Science and Engineering. 2014 ;( 37):81-92. [Persian]
18
[19]. X. Guan, J. Huang1, Y. Zhang1, Y. Xie1 and J. Liu , The relationship between anthropogenic dust and population over global semi-arid regions. Atmos Chem. Phys., , 5159–5169, 2016.
19
[20]. Song H, Wang K, Zhang Y, Hong C, Zhou S. Simulation and evaluation of dust emissions with WRF-Chem (v3. 7.1) and its relationship to the changing climate over East Asia from 1980 to 2015. Atmospheric Environment. 2017 Oct 1;167:511-22.
20
[21]. H. Shen, J.Abuduwail, A. Samat. L. Ma, A review on the research of modern aeolian dust in Central Asia, Arab J Geosci (2016) 9: 625.
21
[22]. Nakhaei S. Water resources allocation considering the effects of climate change in the catchment area (Case study: Sarbaz River). Master's Thesis in Civil Engineering. 2017 July. [Persian]
22
[23]. Alizadeh A. Investigation of groundwater drawdown effect on salinity in close sub-basins with different qualities and surface/groundwater management to control this phenomena. Master's Thesis in Civil Engineering. 2015 Sep. [Persian]
23
[24].Ajamzade, A. WEAP model application guide. 1nd ed. Tehran: Islamic Azad University; 2015. [Persian]
24
[25]. Ramrodi, H, A. Ajdarimoghadam, M. Management of water resources of Pearsarab Uorki region of Sistan and Baluchestan province in case of increasing water requirements by assessing WEAP model. Research in Civil Engineering, Architecture, Urbanism and Sustainable Environment.2015; 1-13. [Persian]
25
[26]. Shahidi, A. Ahmadi, M. Video tutorial model HYDRUS. 1nd ed. Tehran: Kalk Zarin; 2014. [Persian]
26
[27]. Water Resources Development Report. 1nd ed. Tehran: Sistan and Baluchestan Regional Water Company; 2015. [Persian]
27
[28]. Khosravi, M. Long-Term Spatial Analysis of Lake Hamoon. Iranian Water Resources Research Journal.2010 ;( 6):68-79. [Persian]
28
[29]. Mirakzehi K, Pahlavan-Rad MR, Shahriari A, Bameri A. Digital soil mapping of deltaic soils: A case of study from Hirmand (Helmand) river delta. Geoderma. 2018 Mar 1;313:233-40.
29
[30]. Ashrafi ZN, Ghasemian M, Shahrestani MI, Khodabandeh E, Sedaghat A. Evaluation of hydrogen production from harvesting wind energy at high altitudes in Iran by three extrapolating Weibull methods. International Journal of Hydrogen Energy. 2018 Jan 17.
30
[31]. Kamali S, Mofidi A, Zarrin A, Nazaripour H. Sensitivity studies of the forth-generation regional climate model simulation of dust storms in the Sistan plain, Iran. Modeling Earth Systems and Environment. 2017 Jun 1;3(2):769-81.
31
[32]. Ashrafi K, Motlagh MS, Neyestani SE. Dust storms modeling and their impacts on air quality and radiation budget over Iran using WRF-Chem. Air Quality, Atmosphere & Health. 2017 Nov 1;10(9):1059-76.
32
[33]. Alizadeh-Choobari O, Zawar-Reza P, Sturman A. The “wind of 120 days” and dust storm activity over the Sistan Basin. Atmospheric research. 2014;143:328-41.
33
[34]. Zamani, Y. Managing the utilization of water resources, taking into account the need for stabilization of the microstates and evaluating different scenarios. Master's Thesis in Civil Engineering. 2015. [Persian]
34
[35]. Report of first phase water supply studies in Sistan Plain (Sistan Subsoil Water Resources. Regional water company in Sistan and Baluchestan province.2013[Persian]
35
[36]. Meteorological Organization of Sistan and Baluchestan Province, 1395. [Persian]
36
[37]. Amiri, Sh. Mahdavimoghadam, M. Investigation of water resources management in the catchment area using WEAP model (Case study of Hamoon Hearmand Basin). National Conference on Water Crisis Advances in Iran and the Middle East.2014 ;( 2):1-9. [Persian]
37
[38]. Chepil, W, S. Woodruff, N, P. The Phtsics of Wind Erosion and Its Control. 1nd ed. United States: Deportment of Agriculture; 2010.
38
[39]. Operations Report and Soil Mechanics Studies of Sistan Regional Administrative Park Project.2014. [Persian]
39
ORIGINAL_ARTICLE
Integrated management of water demand by economic approach in Northern Sistan and Baluchestan province
This study survived the factors affecting on water demand in agricultural and domestic consumption, amount of water demand and income and price elasticities in household sector in the north of Sistan and Baluchestan. For this purpose, we used the Stone-Greay utility function in the household sector and considered quantitative and qualitative factors influencing the optimal water resources management in agriculture using the Logit model. The results of water demand function estimation in household sector showed that the demand for per capita in general condition was equivalent to 61.5 cubic meters per year and the extera consumption per capita in this case was 26.8 cubic meters. Also, income and price elasticities of household water demand in this case were equal to 282.0 and 373.0 respectively, indicating that the demand for water was less tangible than price; Income stretch is less than one, indicating the necessity of water. The results of estimation of the optimum water consumption model showed that the most effect was on the work experience and history variables and then the variables of using fertilizers and chemical pesticides, type of communication channels, irrigation methods, education and education level had the most effect on optimal use of agricultural water resources.
https://ije.ut.ac.ir/article_67722_3e4023276fbc7cb97795c10789374176.pdf
2018-09-23
1037
1049
10.22059/ije.2018.248647.797
Water demand management
Stone-Greay utility function
Logit Model
the north of Sistan and Balouchestan
Javad
Shahraki
j.shahraki@eco.usb.ac.ir
1
Associate Professor of Sistan and Balouchestan
LEAD_AUTHOR
Ali
Rahnama
ali.rahnama65@gmail.com
2
Phd student of Sistan and Balouchestan
AUTHOR
Hamideh
Khaksar Astaneh
letter.i2017@gmail.com
3
Faculty Member of Tourism Economics Department of Academic center for Education, Culture and Research
AUTHOR
[1]. Mousavi SN, Kavoosi Kalashami M. Evaluation of seasonal, ANN, and hybrid models in modeling urban water consumption: A case study of Rash city. Journal of Water and Wastewater. 2016; 27(4): 93-98. [Persian].
1
[2]. Shahbazi Alamouti A. Water crisis, national problem, requires national resolve. Consulting Engineer Quarterly. 2015; 69: 13-19. [Persian].
2
[3]. Ghafari A, Shirvan J. A review of Iran's water resources utilization status. 9th national conference for irrigation and drainage committee of Iran. 1994. [Persian].
3
[4]. Keshavarz A, Dehghani Sanij, H. Water productivity index and future strategy of the country. Quarterly Journal of Economic Strategy. 2012; 1(1): 199-233. [Persian].
4
[5]. Sabouhi M, Soltani Gh, Beauty M. Investigating the effect of changing price of irrigation water on private and social benefits using a positive mathematical programming. Journal of Agricultural Science and Technology. 2007; 21(1): 53-71.
5
[6]. Mozaffari MM. Determination of the appropriate policy programming to conservation of water resources in Qazvin plain. Journal of Water and Soil Resources Conservation. 2015; 5(2): 29-46.
6
[7]. Bakhshi A, Moghaddasi R, Daneshvar kakhki M. An application of positive mathematical programming model to analyze the effects of alternative policies to water pricing in mashhad. Journal of Agricultural Economics and Development. 2011; 25(3): 284-294. [Persian].
7
[8]. Hojipor M, Zakerinia M, Ziaei AN, Hesam M. Water demand management in agriculture and its impact on water resources of Bojnourd basin with WEAP and MODFLOW models. Journal of Water and Soil Conservation. 2015; 22(4): 85-102. [Persian].
8
[9]. Louckas DP, Beek E, Stedinger JR, Dijkman JPM, Villars MT. Water resources system planning and management an introduction to method, models and application. Published by United Nation Educational Scientific and Cultural Organization. 2005.
9
[10]. Saeidinia M, Samadi Brujeni H, Arab D, Fattahi R. 2008. Investigate transferring water from the Karoon branches adjacent to the basin by using WEAP model (Case study: Behesht Abad Tunnel). Water Res. J. 2008; 3: 33-44.
10
[11]. Bani Habib MA, Shabestari MA, Hosseinzadeh M. A hybrid model for strategic management of agricultural water demand in arid regions. Journal of Iranian Water Resources Research. 2016; 12(4): 60-69. [Persian].
11
[12]. LiuS, Gikas P, Papageorgioua L. An optimization-based approach for integrated water resources management. 20th European Symposium on Computer Aided Process Engineering. 2010.
12
[13]. Shahateet MI. An econometric model for water sector in Jordan. Journal of Social Sciences. 2008; 4(4): 264-271.
13
[14]. Bilali Moghadam b, Dararbi, M.H. Hamedan water demand forecast using artificial neural networks. Haft Hesar: Journal of Environmental Studies. 2016; 15(4): 71-81. [Persian].
14
[15]. Mousavi SN, Moahmmadi H, Boostani F. Estimation of water demand function for urban households: A case study in city of Marvedasht. Journal of Water and Wastewater. 2010; 21(2): 90-94. [Persian].
15
[16]. Alamarah AR. Using socio economic indicators for integrated water resources for integrated water resources management (demand management-case study). The 4th World Water Forum, 16-22 March. 2006. Mexico.
16
[17]. Yuan Zhou Y, Tol R. Water use in China’s domestic, industrial and agricultural sectors: an empirical analysis. Working Papers, Research Unit Sustainability and Global Change, Hamburg University and Centre for Marine and Atmospheric Science. 2005.
17
[18]. Chen Y, Zhang D, Sun Y, Liu X, Wang N, Savenije HG. Water demand management: A case study of the Heihe River Basin in China. Physics and Chemistry of the Earth, Parts A/B/C, Volume 30, Issues 6–7, Pages 408-419. 2005.
18
[19]. Fathis F. Lawgali. Forecasting water demand for agricultural, industrial and domestic use in Libya. International Review of Business Research Papers. 2008; Vol.4 No: 231-248.
19
[20]. Karimanzira D, Jacobi M. A feasible and adaptive water allocation model based on effective water demand. Fraunhofer Center for Applied Systems Technology (AST). 2008.
20
[21]. Shajari Sh, Barikani A, Amjadi A. Water demand management using water pricing policy in Jahrom's palm trees: Case study of Shahani Date. Journal of Agricultural Economics and Development. 2009; 17(65): 55-72. [Persian].
21
[22]. Sabouhi M, Nobakht M. Estimating the water demand function of Pardis city. Journal of Water and Wastewater. 2009; 20(2): 69-74. [Persian].
22
[23]. Khosh-Akhlagh R, Shahraki J. Estimating urban residential water demand for Zahedan. The Economic Research (Scientific Research Quarterly). 2009; 8(4): 129-145.
23
[24]. Sistan and Baluchestan Regional water Authority. Reporting of statistics and basic information on water resources. 2017.
24
ORIGINAL_ARTICLE
Determine of the Actual and Potential Evapotranspiration and Appropriate Model for Determining Water Requirement of Saffron (Case study: Torbat Heydarieh)
The semi-tropical plant of saffron, due to low water requirement and high income, has a special place in the cultivating pattern of arid and semi-arid regions such as Torbat Heydarieh region of Khorasan Razavi. In this research was carried out to determine the potential and actual evapotranspiration rate and the most suitable model for estimating evapotranspiration of saffron in Torbat Heydarieh (the world's saffron producing pole). The results of comparison of different methods with FAO method as standard and basic method showed that Blaney-Criddle, Genesis and Hargreaves methods were more accurately than other methods. A comparison of the results of the FAO method with other methods was performed using Chi-square test. The amount of annual water requirement of saffron in the climate of Torbat Heydarieh using the FAO method was 1731 m3 / ha. Since in the Hargreaves-Samani equation, for the calculation of evapotranspiration, only two factors are necessary for temperature and solar radiation, and it is possible to determine the factors in most weather stations, the overall result of this research is that, in the estimation of initial and Rapid need for a saffron plant in the region can be used.
https://ije.ut.ac.ir/article_67723_972d389521b39fe9995104572448fad5.pdf
2018-09-23
1051
1061
10.22059/ije.2018.252321.830
FAO-Penman-Monteith
Saffron production pole
Water requirement
PARVIN
ALIAKBARI
p_aliakbari89@yahoo.com
1
MSC. RESEARCHER IN SAFFRON INSTITUTE
AUTHOR
amir
salari
salari.1361@yahoo.com
2
Assistant Professor, Department of Plant Production, Faculty of Agricultural and Natural Resources, University of Torbat Heydarieh,
LEAD_AUTHOR
Abbas
KhasheiSiuki
abbaskhashei@birjand.ac.ir
3
university of birjand, Avini street, birjand city, soth khorasan province,iran
AUTHOR
[1]. Ehsanzadoh P, Yadollahi A.A, Maibodi A.N.M. Productivity, growth and quality attributes of 10 Iranian saffron accessions under climatic conditions of Chahar–Mahal Bakhtrazi, Central Iran. In: Proceeding of the 1st International Symposium on Saffron. Albacete. Spain. 2004. p. 183-188.
1
[2]. Mollafilabi A. Experimental findings of production and echophysiological aspects of saffron (Crocus sativus L.). Acta Horticulturae (ISHS), 2004; 650: 195-200.
2
[3]. Kafi M, Koocheki A, Rashed M.H, Nassiri M. (Eds.). Saffron (Crocus sativus) Production and Processing. Science Publishers, Enfield. (In Persian). 2006.
3
[4]. Kafi M, Rashed Mohasel M.H, Koocheki A, Molafilabi A. Saffron Production and Processing. Ferdowsi University of Mashhad Publications, Mashhad, Iran. 2002. (In Persian).
4
[5]. Rashed M.H, Kafi M, Koocheki A, Nassiri M. Saffron (Crocus sativus) Production and Processing. Science Publications, 2006; 87-96. (In Persian).
5
[6]. Bari Abarghoei H, Ghalavand A, Mazaheri D, Noor Mohammadi G, Sanei M. Temperature effect on flowering and yield performance accessions on Iranian saffron. Pajouhesh Va Sazandgi, 2001; 4: 65-69. (In Persian with English Summary).
6
[7]. Stegman E.C, Bauer A. Sugar beet response to water stress in sandy soils. Transaction of the American Society of Agriculture Engineering, 1977; 20: 469-472.
7
[8]. Khashei Siuki A, Hashemi S.R, Ahmadee M. The effect of Pottasic Zeolite and irrigation scheduling on saffron yield. Reserch Project in University of Birjand. 2015. (In Parsian).
8
[9]. Burman R, Pochop L.O. Evaporation, Evapotranspiration and Climate Data. Elsevier Science B.V. 1994.
9
[10]. Allen R.G, Smith M, Pereira L.S, Raes D. An update for the calculation of reference evapotranspiration. ICID Bulletin, 1994; 43(2): 35-92.
10
[11]. Allen R.G, Pereira L.S, Raes D, Smith M. Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper, 1998; NO. 56. Rome. Italy.
11
[12]. Hargreaves G.H. Defining and using reference evapotranspiration. Irrigation and Drainage Engineering ASCE, 1994; 120(6): 1132-1139.
12
[13]. Jensen ME, Burman R.D, Allen R.G. Evapotranspiration and Irrigation Water Requirements. ASCE Manuals and Reports on Engineering Practices, 1990; No. 70. American Society of Civil Engineers, NY.
13
[14]. Shih S.F. Data requirement for evapotranspiration estimation. Irrigation and Drainage Engineering. ASCE, 1984; 110(3): 263-274.
14
[15]. Amatya D.M, Skaggs R.W, Gregory J.D. Comparison of methods for estimating REF-ET. Irrigation and Drainage Engineering, 1995; 121(6): 427-435.
15
[16]. Saeed M. The estimation of evapotranspiration by some equations under hot and arid conditions. Transaction American Society of Agricultural and Biological Engineers, 1986; 29(2): 434-438.
16
[17]. Baiat Varkeshi M, Zare Abyaneh H, Ghasemi A. Provide the Best Empirical Evapotranspiration Relationship Compared With FAO-Penman-Monteith in the North West. 3rd Iran Water Resources Management Conference. Tabriz, Iran. 2008. (In Persian)
17
[18]. Zandilak H. Select the Appropriate Method for Estimating Evapotranspiration in Yazd. 1st Regional Water Resources Development Conference. Abarkoh. 2011. (In Persian).
18
[19]. Zare Abianeh H, Biat Varkeshi M, Sabzi Parvar AK, Maroofi S, Ghasemi A. Evaluation of estimating reference evapotranspiration methods in Iran. Journal of natural geographic researches. 2010; 74: 95-110. [Persian].
19
[20]. FallahGhalhari GA, Ahmadi H. The estimation of phenological thresholds of Saffron cultivation in Isfahan province based on the daily temperature statistics, Saffron Agronomy and Technology, 2015; 3 (1):65-49. [In Persian].
20
[21]. Fooladmand H. R, Sepaskhah A. R. Evaluation and calibration of three evapotranspiration equations in a Semi-Arid region. 2005.
21
[22]. Nasaji Zavareh M, Sadeghifar R. Estimation of reference crop evapotranspiration using different methods (Case study: Karaj). 9th Conference on Irrigation and Evaporation Reduction. Kerman, Iran. 2007. (In Persian).
22
[23]. Pakdin M, Shahnavaz Y, Roostaei S, Alipoor H. Study of potential and actual evapotranspiration in Faruj basin. The 1st National Conference on Solutions to Access Sustainable Development in Agriculture, Natural Resources and the Environment (sdconf). Tehran, Iran. 2012. (In Persian).
23
[24]. Nazari R, Kaviani A. Evaluation of potential evapotranspiration methods and evaporation pan with lysimeter in semiarid climate (case study: Qazvin plain). Ecohydrology journal. 2016; 3(1): 19-30.
24
[25]. Koochakzadeh M, Nikbakht G. Evaluating of reference evapotranspiration methods with FAO-56 in different climate in Iran. Journal of Agricultural science. 2003; 10(3): 43-57. [Persian]
25
[26]. Ahmadee M, Khashei Siuki A, Sayyari, M.H. Comparison efficiency of different equations to estimate the water requirement in saffron (Crocus sativus L.) (Case study: Birjand plain, Iran). Journal of Agroecology, 2017; 8(4): 505-520. (In Persian with English Summary).
26
[27]. Sepaskhah A.R, Kamgar-Haghighi A.A. Saffron irrigation regime. International Journal of Plant Production, 2009; 3(1): 1-16.
27
[28]. Asghari Jafarabadi M, Mohammadi SM. Statistical Series: An Introduction to Inferential Statistics (Point Estimation, Confidence Interval anHypothesisTesting). Journal of Diabetes and Metabolic Disorders2013Under Press [In Persian].
28
[29]. Samadi H, Majdzadeh B. Comparison of reference evapotranspiration calculated by empirical formulas with lysimeters in Kerman. 8th Conference on Irrigation and Evaporation Reduction. Kerman, Iran. 2003. (In Persian).
29
[30]. Alizadeh A, Mahdavi M, Iranloo M, Bazari M.E. Evapo-transpiration and crop coefficient of saffron (Crocus sativus). Geographical Research, 1999; 54 and 55: 29-42. (In Persian with English Summary).
30
[31]. Mahdavi M. Plant coefficient and saffron evapotranspiration on standard condition. MSc thesis. Faculty of Agricultutre. Ferdowsi University of Mashhad, Iran. 1999. (In Parsian)
31
[32]. Doorenbos J, Pruitt W.O. Guidelines for predicting crop water requirements. FAO irrigation and drainage paper, 1977; NO. 24. Rome. Italy.
32
[33]. Salih A.M.A, Sendil U. Evapotranspiration under extremely arid climates. Irrigation and Drainage Engineering, ASCE, 1984; 110 (3): 289-303.
33